Noninvasive system and method for identifying coronary disfunction utilizing electrocardiography derived data

ABSTRACT

A system and method for analyzing experimentally obtained electrocardiograph ECG data, which allows accurate catagorization of subjects into various abnormal and normal classifications, and which allows acurate tracking of subject cardiac status change, is disclosed. The method applies an algorithm which compares representative parameter, (eg. root-mean-square RMS mean), values derived from analysis of a representative composite of selected portions of a number of ECG PQRST waveforms obtained from ECG investigation of a subject, to similarly derived representative parameter, (eg. RMS mean and RMS standard deviation), values present in a compiled data bank derived from ECG investigation of numerous normals, (or initial subject data), in each of a plurality of frequency range bands. A highly diagnostic numerical &#34;Score&#34; is calculated by addition of &#34;Score&#34; components found to be acceptable under certain mathematical criteria, and provided by the algorithm. Visually interpretable power spectral density plots supplement the method. In addition, comparison of the calculated &#34;Score&#34; to subject cardiac ejection fraction provides indication of risk for sudden death. The present invention is directly adapted to tracking subject cardiac status change by substituting a baseline subject data set for the normal population data set.

This is a Continuation-In-Part application based upon a Copending U.S.application Ser. No. 08/418,175 filed Apr. 6, 1995, now U.S. Pat. No.5,655,540.

TECHNICAL FIELD

The present invention relates to safe noninvasive systems and methods ofuse thereof for application in identifying coronary disfunction, whichmethods and systems are suitable for application in investigation ofhuman subjects. More particularly the present invention is primarily amethod of processing data derived from application of anelectrocardiograph (ECG) system which involves mathematical andstatistical techniques, data filtering and windowing, and application ofa unique algorithm to the end that highly predictive, easily interpretednumerically precise SEECADtm "Scores" and data patterns are determined.

BACKGROUND

It is generally accepted that approximately one-quarter of NorthAmericans have some degree of Coronary Artery Disease (CAD). It is alsogenerally accepted that approximately half thereof are not reliablydetectable by conventionally applied diagnostic techniques, such asnoting contour changes in the S-T segments of electrocardiograms(ECG's). The problematic nature the situation poses is perhaps mostcritically apparent when one considers that perioperative complicationscan be much more prevalent and serious in a patient with (CAD) than in anormal patient who does not have (CAD). That is, knowledge that apatient has (CAD), or any coronary dysfunction, can be critical infostering morbidity and mortality reduction procedure planning andscheduling on the part of medical professionals. As well, detection of(CAD) is, of course, important in everyday matters such as the planningand execution of a simple exercise routine.

It is noted that conventional (ECG) analysis provides time domaingraphical results based primarily upon the low frequency (eg. 0 to 40Hz), content of a subject's cardiac signals monitored by an (ECG)system. This is the case whether a Frank orthogonal X-Y-Z; a standard 12Lead or a multi-Lead monitoring etc. (ECG) system is utilized. Forinstance, a Patent to Brown et al., U.S. Pat. No. 5,077,667 describes amethod for measuring a clinically useful characteristic of afibrillating heart related to the elapsed time since the onset ofventricular fibrillation. Their significant variable is the power in thefrequency range of 7 to 8 Hz. The Brown et al. Patent describes the useof a transformation of sampled analog time domain signals into thefrequency domain and subsequent analysis thereof as a step intermediateto applying corrective treatment to a fibrillating heart.

A Patent, U.S. Pat. No. 4,680,708 to Ambos et al., describes the use ofa Fast Fourier Transform (FFT) applied to a portion of an (ECG) cycle.Mathematical analysis of the last forty (40) milliseconds of a waveformderived from the time domain (QRS) complex allows for calculation of aFigure Of Merit, (FOM), based upon the frequency content thereof. Said(FOM) is correlated to the likelihood of a patient experiencingventricular tachycardia. While the Ambos et al. Patent mentions thepresence of high frequency components in a signal derived from the(ECG), said Patent primarily focuses upon the analysis of frequenciesbetween 20 and 50 Hertz in arriving at the (FOM). The Ambos et al.Patent further states that "Recent studies . . . have used a variety oflow (25 to 100 Hz) and high (250 to 300 Hz) band pass filters. A majorlimitation . . . is a lack of a-priori knowledge of the frequencydistribution of signals of interest and the inherent risk that filteringwill exclude signals of particular interest."

Other recent investigation has focused upon the diagnostic capabilityinherent in the presence of particular high frequency components presentin an (ECG) signal. For instance, a very recent paper by Aversano etal., titled "High Frequency QRS Electrocardiography In The Detection OfReperfusion Following Thrombolytic Therapy", (see Clinical Cardiology,(17, 175-182, April 1994)), states that the amplitude of the highfrequency components, (eg. 150-250 Hz), of the (QRS) complexes decreasesduring cardiac ischemia, and returns to normal with resolution thereof.It is also stated that high frequency electrocardiography is a rapid andreliable bedside technique for discriminating between successful andfailed reperfusion in patients treated with thrombolytic agents formyocardial infarction. The Aversano et al. paper also states that"Studies involving high-frequency QRS electrocardiography are few andmodest."

A paper by Moss and Benhorin titled "Prognosis and Management After aFirst Myocardial Infarction", New England J. Medicine, Vol. 322, No. 11,1990 points out the importance of being able to identify and distinguishpatients with various types of (CAD) so that appropriate treatment canbe prescribed. This paper, in conclusion acknowledges that noninvasivetechniques currently available for detecting jeopardized ischemicmyocardium are imperfect.

The above sampling of relevant prior reference materials shows thattechniques such as direct morphologic analysis of conventional timedomain signals, application of (FFT) to (ECG) time domain derivedsignals to provide frequency domain spectra for analysis, analysis ofhigh frequency components of (ECG) signals and the focusing on specificportions of a QRS complex etc. are known. There remains, however, needfor additional and more probative noninvasive methods of analyzing (ECG)derived data which allow incipient (CAD) in patients to be identifiedwith improved certainty. In particular, there is a need for a method ofaccurately identifying subjects with (CAD), the validity of which hasbeen shown to provide utility by actual clinical testing.

The present invention provides an improved method of analyzing (ECG)derived data which have as shown by actual test, (in view of anextensive data bank accumulated by the inventor containing both normaland abnormal (ECG) data), to enable greatly improved ability toaccurately and noninvasively separate abnormal from normal cardiacsubjects. The method of the present invention, for instance, routinelyallows identification of subjects with truely silent (CAD), and subjectswho do not present with the tell-tale classic QRST and T wave changes.The method of the present invention also routinely allows identificationof subjects with nonspecific S-T and T wave changes, and allowsidentification and separate classification of subjects with priormyocardial infarction, abnormal patients who present with normal (ECG),and simultaneously distinguishes the population of abnormal subjects whopresent with normal (ECG). The present invention also allowsidentification of subjects who are at risk for sudden death.

DISCLOSURE OF THE INVENTION

The present invention, in its presently preferred embodiment, utilizes aFrank orthogonal X-Y-Z Lead electocardiograph (ECG) system, but isprimarily a method of analyzing and categorizing individual subjectelectrocardiogram (ECG) data. Said method has been shown to be capableof identifying and classifying subjects into cardiac categories such as:

1. Normal, and

2. Abnormal:

a. Presents with prior myocardial infarction,

b. Presents with nonspecific S-T and T wave changes,

c. Presents with normal (ECG) but known otherwise to be abnormal.

(Note, continued efforts are serving to greatly increase the specificclassification capabilities of the present invention, and are evenextending it to identification of specific anatomic abnornormalitylocations), (eg. myocardium, cardiac artery etc), which cause saidspecific abnormality).

As a starting point the present invention requires a substantial database from which (ECG) data attributable to a "normal" population can bederived and used to form a "template" against which unknown subject(ECG's) can be compared. The present invention provides that byapplication of a discriminant Algorithm, (see supra), these unknownsubjects may be appropriately classified. In the presently preferredembodiment of the present invention such a data base was developed byselection and testing of fit and healthy, relatively young subjects fromfamilies with a low prevalence of, and low risk factors for, coronaryartery disease (CAD). Suitable subjects were required to fill out aquestionnaire, analysis of which aided in determination of subjectsuitability as a "normal". A total of two-hundred-fifty (250) normalsubjects were identified and (ECG) data obtained from each thereof. Arandom sampling of data from one-hundred-forty (146) subjects from saidgroup of two-hundred-fifty (250) normals was assembled and analyzed toprovide relevant, (see supra), composite root-mean-square, (RMS), meanand standard deviation values.

To arrive at said relevant normal RMS mean and RMS standard deviationvalues, (ECG) signals for each normal were derived by acquiring a numberof, (typically one-hundred (100)), full (ECG) cycles, (ie. full PQRST(ECG) cycles), for each normal subject, sampling each full cycle toprovide six-hundred (600) data points over the extent thereof, and thenselecting out the corresponding data points in each QRS complex in eachof said full PQRST cardiac cycles. A single averaged (ECG) cardiac cyclewas mathematically constructed from said number of QRS complexes and RMSmean and RMS standard deviation values calculated therefore.

In addition, similar RMS means and RMS standard deviation values wereobtained from the same data, but which data had been subjected todigital filtering employing a Blackman-Harris window. The results areidentified in the following table for each of the three Frank orthogonal(ECG) X-Y-Z system lead signals:

    ______________________________________    FREQUENCY RANGE    DATA PROVIDED    ______________________________________    FOR FRANK (ECG) SYSTEM LEAD X, (HORIZONTAL AXIS):    (FULL-ALL FREQUENCIES)                       RMS MEAN  RMS SD    ((0) TO (10) HZ)   RMS MEAN  RMS SD    ((10) TO (60) HZ)  RMS MEAN  RMS SD    ((60) TO (150) HZ) RMS MEAN  RMS SD    ((150) TO (250) HZ)                       RMS MEAN  RMS SD    ______________________________________    FOR FRANK (ECG) SYSTEM LEAD Y, (VERTICAL AXIS):    (FULL-ALL FREQUENCIES)                       RMS MEAN  RMS SD    ((0) TO (10) HZ    RMS MEAN  RMS SD    ((10) TO (60) HZ)  RMS MEAN  RMS SD    ((60) TO (150) HZ) RMS MEAN  RMS SD    ((150) TO (250) HZ)                       RMS MEAN  RMS SD    ______________________________________    FOR FRANK (ECG) SYSTEM LEAD Z, FRONT TO BACK AXIS):    (FULL-ALL FREQUENCIES)                       RMS MEAN  RMS SD    ((0) TO (10) HZ)   RMS MEAN  RMS SD    ((10) TO (60) HZ)  RMS MEAN  RMS SD    ((60) TO (150) HZ) RMS MEAN  RMS SD    ((150) TO (250) HZ)                       RMS MEAN  RMS SD    ______________________________________

where SD stands for Standard Deviation.

                  TABLE D-1    ______________________________________    EACH TABULAR CATEGORY IS PROVIDED AN RMS    MEAN AND STANDARD DEVIATION    FREQUENCY (HZ)                LEAD X      LEAD Y   LEAD Z    ______________________________________    TOTAL SIGNAL     0-INFINITE Hz     0-10 Hz     10-60 Hz     60-150 Hz    150-250 Hz    ______________________________________

Said resulting normal RMS mean and RMS standard deviation values foreach of the frequency ranges and utilized Frank (ECG) system X-Y-Z leadserve to define assumed Gaussian "templates" for normals, against whichsimilarly derived RMS means, acquired from individual subjects, arecompared under the guidelines of the Algorithmic Method of the presentinvention, (see supra).

To date data from more than one-thousand (1000) abnormal subjects hasbeen assembled by the inventor, and subject RMS mean values derivedtherefrom. It is noted that abnormal subjects tested providerepresentatives from each the three abnormal groups identified infra.

Continuing, to apply the Algorithm Method of the present invention many,(typically one-hundred (100)), of human subject derived full PQRST (ECG)cardiac cycles are obtained. Said full (ECG) cardiac cycles are eachthen sampled to provide six-hundred data points over the extent thereofand the sampled data points corresponding to the QRS complex in eachfull cycle are mathematically averaged to provide a composite QRScomplex. As well, the digital filtering and application of theBlackman-Harris Window to allow calculation of the RMS means whichcorrespond to each frequency range and Frank (ECG) system X-Y-Z leadutilized, are performed. The end result can be expressed as a table ofdata, (not shown), similar to the table presented above for the normaldata but which contains only composite RMS mean values.

The Algorithm employed in the method of the present invention embodimentthen provides for a maximum of thirty (30) calculations to be performedas follows:

a. Up to Fifteen of said calculations involve calculating the differencebetween the subject composite RMS mean and the corresponding normalcomposite RMS mean, and dividing the result by the corresponding normalRMS standard deviation, for each Frank X-Y-Z (ECG) lead and eachfrequency range band, (ie. the fifteen calculations break down as five(5) frequency range bands per each of the Frank X-Y-Z (ECG) leads.)

b. Twelve of said calculations involve finding the RMS ratio for eachfrequency range band, (eg. 0-10 Hz, 10-60 Hz, 60-150 Hz and 150-250 Hz)to the total sum of all said frequency band RMS contributions for eachappropriate said Frank (ECG) X-Y-Z lead, for a subject, and subtractingtherefrom equivalent RMS ratio mean results derived based upon the samecalculations as applied to normal data, and dividing the result by theRMS standard deviation for the corresponding frequency range, of saidnormal data.

c. Three of said calculations involve calculating the difference betweenratios of the subject composite RMS means of Frank lead X/Y, Y/Z and X/Zratios and the corresponding RMS means of normal X/Y, Y/Z and X/Z ratiosand dividing by the RMS value of the normal RMS standard deviations ofsaid ratios.

Each of the above identified thirty (30) calculations will result in anumber (Pi). A "Score" component number (Si) is then derived based uponwhere a (Pi) number lies in an assumed Gaussian Distribution. This iscalculated based upon normal data RMS Means and RMS Standard Deviations(X) as follows:

If -1×<Pi<1× then Si=0,

If -2×<Pi<-1× or 1×<Pi<2× then Si=1,

If -3×<Pi<-2× or 2×<Pi<3× then Si=2,

If -4×<Pi<-3× or 3×<Pi<4× then Si=3 etc.

Each of the resulting subject RMS mean values associated with acalculated Si value is then analyzed to determine if it is greater thanor equal to ninety-five (95%) percent of the data points from which thenormal RMS mean was calculated. If this is the case the associated Si isaccepted. Otherwise it is rejected. That is, a ninety-five (95%)confidence interval, based upon normal data spread, is imposed, indetermining whether to accept a calculated (SI) value.

Accepted values are then selected and added together to provide a finalnumerical "Score". (Note, two Scores in the catagory a. above arecommonly not selected as being redundant to other Scores. Said commonlyunselected Scores are better identified in the Detailed DescriptionSection).

(Note that the truncation involved in obtaining an Si value can beeliminated and the Pi Score utilized in determining said final numerical"Score").

In either approach to "Score" calculation it has been found that if saidfinal numerical "Score" is "low"(eq. approximately 0 to 7) then thesubject is more likely to be normal. If the final numerical "Score" ishigh (eg. greater than about 8), then the subject is more likely to beabnormal. For instance, a "Score" of 7 provides a ninety (90%) percentconfidence of normality, and a "Score" of 8.4 provides a ninety-five(95%) confidence of normality, (See FIG. 9).

As will be better presented in the Detailed Description Section of thisDisclosure, the results of the application of the method of the presentinvention as described above can be presented in numerous ways. Aparticularly relevant approach is to present the results on a graph of("Sensitivity"vs. "100-Specificity"). (The present invention providesthat the "Score" value be plotted against the abscissa (100-Specificity)and that percentage of a group having said "Score" be plotted on the(Sensitivity) ordinate). Said approach to presentation is generallyknown as an ROC curve, (ROC stands for Receiver Operation Characteristicas the technique was originally derived for use in testing radioreceiver quality). Said approach to presentation serves to visuallydemonstrate the success of the present invention method of analyzing(ECG) derived data. In particular, abnormal subjects which can not beidentified by conventional (ECG) analysis techniques, are seen to beeasily identified by application of the present invention algorithm.

In addition, the present invention provides that time domain dataobtained from Frank X-Y-Z (ECG) leads should be subjected to a FourierTransform and manipulated to provide Power Spectral Density (PDS) vs.Frequency plots. As will also be better presented in the DetailedDescription Section of this Disclosure, said (PDS) plots are typicallyeasier than associated (ECG) data vs. time plots to visually interpret.Said plots complement the above described "Scoring" system approach toidentifying coronary disfunction.

It should also be appreciated that if an initial patient specific database is accumulated, it can serve as a baseline data base and beutilized as a replacement for the normal subject population data base.At later times, additional patient specific data can then be obtained,and compared to the patient baseline data base, in a tracking scenario.

It has further been found that, if dividing a "Score" for a patient asprovided by practice of the present invention, by the ejection fractionof the patient, (as obtained by radionuclide imaging or other accuratetechnique), provides a result greater than one (1.0) then the subjectpatient involved is at high risk of sudden death. In addition, it hasbeen found that if the S-T segement following a QRS complex has"Rhomboids" present therein, (eg. electrical signal activity on theorder of three (3) standard deviations from a baseline signal,particulary in Time Domain plots in 60-150 and/or 150-250 Hz band(s)),then the patient involved is at high risk of sudden death. The presentinvention methodology can include as steps inclusion of said criteria.

A system for practicing the present invention method comprises (ECG)signal monitoring electrode means, (perhaps preferably such as describedin an Allowed U.S. Patent to Stratbucker, Ser. No. 434,658 which claimsa Bioelectric Interface with all necessary (ECG) Chest MountedElectrodes present in a common electrode separation maintaining supportmaterial), and any necessary interface such that data monitored by said(ECG) electrodes is fed to a memory device, and possibly means fordetermining ejection fraction. In addition, computational means forperforming necessary calculations and displaying results, and necessaryinterconnection and interfacing means are required. It should beappreciated that a system for practicing the present invention methodcan be fashioned from essentially any computer system with sufficientmemory means and computational capability means, and it is theconfiguration thereof to carry out the method of the present inventionwhich distinguishes said system over computer systems in general, ratherthan any specific system elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a shows a representation of a human torso with Frank X and Y (ECG)system leads attached thereto.

FIG. 1b shows a cross section taken at a--a in FIG. 1a, with Frank Z(ECG) system leads attached thereto.

FIG. 2 shows a demonstrative "PQRST" (ECG) waveform.

FIG. 3 demonstrates a table for recording data necessary for practice ofthe present invention.

FIG. 4 shows an assumed gaussian distribution for use in assigning"Score" component numbers during practice of the present algorithm.

FIG. 5 shows sample results as provided by practice of the presentinvention plotted on an ROC curve in which a (100-Specificity), andSensitivity, appear on the abscissa and ordinate respectively.

FIGS. 6X1, 6X2, 6X3, 6X4, 6Y1, 6Y2, 6Y3, 6Y4, 6Z1, 6Z2, 6Z3, 6Z4), showtwelve related plots of subject time domain sample data recorded fromFrank X-Y and Z (ECG) system leads for a plurality of frequency rangebands. Higher frequency frequency bands are presented as one progressesfrom FIG. 6X1 to 6X4, and from 6Y1 to 6Y4 and from 6Z1 to 6Z4.

FIGS. 7aX1, 7aX2, 7aX3, 7aX4, 7aX5, 7aY1, 7aY2, 7aY3, 7aY4, 7aY5, 7aZ1,7aZ2, 7aZ3, 7aZ4, 7aZ5, show fifteen related plots of subject frequencydomain power spectral density. Shown in (7aX1, 7aY1 and 7aZ1 aretransforms of full frequency band requisite data. Transforms of datafrom progressively higher frequency frequency bands are presented as oneprogresses from FIG. 7aX1 to 7aX5, from 7aY1 to 7aY5 and from 7aZ1 to7aZ5.

FIGS. 7bX1, 7bX2, 7bX3, 7bX4, 7bX5), 7bY1, 7bY2, 7bY3, 7bY4, 7bY5, 7bZ1,7bZ2, 7bZ3, 7bZ4, 7bZ5, show fifteen plots of related subject timedomain data. Shown in (7bX1, 7bY1 and 7bZ1) are full frequency bandrequisite data. Data from higher frequency frequency bands are presentedas one progresses progresses from FIG. 7BX1 to 7aY5, from 7aY1 to 7aY5and from 7aZ1 to 7aZ5.

FIG. 8 shows actual data obtained from various subject groups bypractice of the present invention, on a ROC curve

FIG. 9 shows actual data obtained from various subject groups bypractice of the present invention, on a graph in which the abcsissa isscaled linearly with the present invention "Score".

FIGS. 10a, 10b, 10c, and 10d are flow charts showing aspects andmodifications of present invention method.

FIGS. 11a and 11b show resepctively, presentation of data for subjectsat risk of sudden death and subjects not at risk of sudden deathproduced by present invention methodology.

FIG. 12 shows a presentation of data developed by present inventionmethodology and indicates that patterns of subject abnormality can beobserved in such a presentation.

FIG. 13 shows a system for practicing the present invention method.

DETAILED DESCRIPTION

In the following a specific embodiment of the present invention ispresented. Said specific embodiment assumes the use of an (ECG) systemwhich utilizes Frank (ECG) orthogonal X-Y-Z leads. It is to beunderstood, however, that the present invention is not limited to suchand can be practiced with (ECG) systems in which any number of leads,(eg. standard twelve (12), sixteen (16), or mapping arrays oftwenty-four (24) or more etc.), are present, and in which only some ofthe present leads are utilized.

The following specific embodiment is presented as it is well documentedand is presently the preferred embodiment.

Turning now to the drawings, there is shown in FIG. 1(a) a frontal viewof a torso of a human, with (ECG) Frank X and Y leads properly affixedthereto. FIG. 1(b) shows a cross section taken at a--a in FIG. 1(a) with(ECG) Frank Z leads properly attached thereto. In use said (ECG) FrankX-Y-Z leads are attached to an (ECG) system and serve to effectorthogonal monitoring of (ECG) full cardiac cycle PQRST signals whichare essentially shaped as shown in FIG. 2.

The present invention requires as a starting point that a significantdata base be available, which significant data base containsrepresentative composite (ECG) data for all, or some portion of full(ECG) PQRST cycles for each (ECG) lead, for a normal population. (Note,a normal population is defined as one in which the subjects have nodetectable coronary artery disease (CAD) by history and multipleconventional diagnostic tests, and are not at risk therefore based uponage, family history etc.) Such a significant data base for normals wasacquired by obtaining a number of full (ECG) PQRST cycles from each ofthe Frank X-Y-Z (ECG) leads present in the presently discussedembodiment of the invention, from each of two-hundred-fifty (250)normals. (It is noted that the present invention is not limited to casesin which all leads present in an (ECG) system are monitored but that apreferred embodiment does utilize all available information). Next, arandom sample of one-hundred-forty-six (146) of said two-hundred-fifty(250) normals was selected and a representative number of the full (ECG)PQRST cycles from each, (typically one-hundred (100)), for each FrankX-Y-Z (ECG) lead, were then selected and each subjected to a samplingprocedure which provided some number of data points for each,(six-hundred (600) was chosen in the presently discussed embodiment).Next, the sampled data points corresponding to the QRS depolarizationcomplexes in each selected full (ECG) PQRST cycle were selected and arepresentative composite QRS complex for each Frank (ECG) X-Y and Zleads formed therefrom by mathematical averaging thereof. Saidrepresentative composite was then subjected to filtering and windowingtechniques to provide a number of data sets for each of the Frank (ECG)X-Y-Z leads. Said data sets in the presently preferred embodiment of thepresent invention provide information present in said representativecomposite in the frequency bands:

a. All frequencies;

b. Between zero (0) and ten (10) Hz;

C. Between ten (10) and sixty (60) Hz;

d. Between sixty (60) and

e. Between one-hundred-fifty (150) and two-hundred-fifty (250) Hz.

For each of the Frank (ECG) X-Y-Z leads then, five (5) sets of data werederived as described, and from each of said sets of data aRoot-Mean-Square (RMS) mean and (RMS) standard deviation werecalculated. This, it will be appreciated, resulted in fifteen (15) RMSrepresentative composite means and standard deviations being available,(five for each Frank (ECG) X-Y-Z lead).

In view of the described RMS mean and RMS standard deviations (SD's)available clinical application of the present invention can bepracticed.

To practice the present invention, data are obtained from a subject in amanner essentially the same as described infra for normals. That is, anumber of full (ECG) PQRST full cardiac cycles from each Frank (ECG)X-Y-Z lead are obtained and a representative number thereof are selectedand subjected to a sampling procedure. Some portion of each full PQRSTwaveform is selected, (eg. the QRS depolarization complex is utilized inthe presently preferred embodiment of the present invention), and arepresentative composite thereof formed therefrom for each Frank (ECG)system X-Y-Z lead. For each representative composite a RMS mean is thencalculated so that a table equivalent to that shown in FIG. 3, butcontaining subject RMS mean data, is formed.

With the described normal RMS mean and RMS standard deviation data, andsubject RMS mean data then available, the algorithm of the method of thepresent invention can be applied to arrive at a diagnostic mathematical"Score".

The algorithm of the present invention involves mathematical comparisonof:

a. Normal and subject RMS means in view of normal RMS standarddeviation;

b. Ratios of normal and subject RNS frequency range and means to thesummation of RMS means for all Frequency range bands for each Frank(ECG) X-Y-Z lead in view of normal standard deviation for the numeratorfrequency band.

c. Ratios of normal and subject Frank (ECG) X-Y-Z lead RMS means in viewof normal standard deviations of said ratios.

Briefly, application of each of the identified steps provides anumerical result (Pi), which in general is typically not a wholeinteger. The next step is to process said numerical result (Pi) bycomparison to an assumed Gaussian Distribution derived from the normalpopulation data to arrive at a whole number integer which represents howmany RMS standard deviations the subject RMS mean is away from thenormal RMS mean, and assign a whole integer "Score" component number(Si) based thereupon. The algorithm then requires that a ninety-five(95%) confidence interval, based upon normal RMS standard deviation databe applied to determine if a "Score" component should be accepted andincluded in calculation of a final "Score", said final "Score" beingarrived at by an addition of accepted "Score" components. Said algorithmwill now be described in detail.

The first step in applying the algorithm of the presently describedpresently preferred embodiment of the present invention is perform up tofifteen (15) calculations comprising subtracting the Subject RMS meanfrom a corresponding Normal RMS mean and dividing the result by acorresponding normal RMS standard deviation for each Frank (ECG) X-Y-Zlead in each frequency range identified infra, to provide numbers (Pi).

(Note, the accompany computer printout page labeled "avgasc" providesthe various RMS Means and Standard Deviations referred to in thefollowing, corresponding to the P1-P30 number.)

For the Frank (ECG) X lead, (IE. HORIZONTAL AXIS)

For all frequencies: ##EQU1##

For the frequency range band zero (0) to ten (10 Hz): ##EQU2##

For the frequency range band ten (10) to sixty (60 Hz): ##EQU3##

For the frequency range band sixty (60) to ne-hundred-fifty (150 Hz):##EQU4##

For the frequency range band one-hundred-fifty (150 Hz) totwo-hundred-fifty (250 Hz): ##EQU5## For the Frank (ECG) Y lead, (IE.VERTICAL AXIS)

For all frequencies: ##EQU6##

For the frequency range band zero (0) to ten (10 Hz): ##EQU7##

For the frequency range band ten (10) to sixty (60 Hz): ##EQU8##

For the frequency range band sixty (60) to one-hundred-fifty (150 Hz):##EQU9##

For the frequency range band one-hundred-fifty (150 Hz) totwo-hundred-fifty (250 Hz): ##EQU10## For the Frank (ECG) Z lead. (IE.FRONT TO BACK AXIS)

For all frequencies: ##EQU11##

For the frequency range band zero (0) to ten (10 Hz): ##EQU12##

For the frequency range band ten (10) to sixty (60 Hz): ##EQU13##

For the frequency range band sixty (60) to one-hundred-fifty (150 Hz):##EQU14##

For the frequency range band one-hundred-fifty (150 Hz) totwo-hundred-fifty (250 Hz): ##EQU15##

Twelve (12) additional groups of calculations are then performed inwhich the relative RMS mean content of each frequency range bandidentified infra is determined as a percentage of the RMS means of thesum of the filter derived frequency range bands for each Frank (ECG)system X-Y-Z system lead, for both Subject and Normal data, thedifferences therebetween being divided by the corresponding normal RMSStandard Deviation to provide additional numbers (Pi):

For Frank (ECG) X lead. (IE. HORIZONTAL AXIS

Define: ##EQU16##

Then: ##EQU17##

Define: ##EQU18##

Then: ##EQU19##

Define: ##EQU20##

Then: ##EQU21##

Define: ##EQU22##

Then: ##EQU23## For Frank (ECG) Y lead, (IE. VERTICAL AXIS)

Define: ##EQU24##

Then: ##EQU25##

Define: ##EQU26##

Then: ##EQU27##

Define: ##EQU28##

Then: ##EQU29##

Define: ##EQU30##

Then: ##EQU31## For Frank (ECG) Z lead, (IE. FRONT TO BACK AXIS)

Define: ##EQU32##

Then: ##EQU33##

Define: ##EQU34##

Then: ##EQU35##

Define: ##EQU36##

Then: ##EQU37##

Define: ##EQU38##

Then: ##EQU39##

Three (3) additional calculations are then performed in which SubjectRMS means of ratios of RMS means obtained from the Frank (ECG) X-Y-Zleads, are subtracted from corresponding RMS means of ratios obtainedsimilarly from normals, the results of which subtraction are thendivided by RMS Standard Deviations of normal corresponding RMS ratios toprovide additional numbers (Pi):

For the Frank (ECG) X-Y-Z leads ##EQU40##

Continuing, each of the above up to thirty (30) calculated numbers:

(PX1-P1, PX2-P2, PX3-P3, PX4-P4, PX5-P5, PX6-P6 PX7-P7, PX8-P8, PX9-P9),(PY1-P10, PY2-P11, PY3-P12, PY4-P14, PY6-P15, PY7-P16, PY8-P17,PY9-P18), (PZ1l-P19, PZ2-P20, PZ3-P21, PZ4-P22, PZ5-P23, PZ6-P24,PZ7-P25, PZ8-P26, PZ9-P27), P(X/Y-P28), P(Y/Z)-P29 and P(X/Z)-P30),(generally identified as (Pi)),

can optionally be compared to a corresponding assumed distribution ofnormal data to arrive at a "Score" component number. If a number (Pi) iswithin some ± "X" RMS Standard Deviation range of the RMS mean as shownbelow, a "Score" component number (Si) is taken to be:

If -1×<(Pi)<1× then Si=0;

If -2×<(Pi)<-1× or

If 1×<(Pi)<2× then Si=1;

If -3×<(Pi)<-2× or

If 2×<(Pi)<3× then Si=2;

If -4×<(Pi)<-3× or

If 3×<(Pi)<4× then Si=3 etc.

FIG. 4 demonstrates this graphically.

Continuing, each resulting "Score" component (PI) or (Si) calculated asjust described is then subjected to a final test to determine if itshould be accepted or rejected. Said final test involves comparing theSubject RMS mean to the data from which the Normal RMS mean wascalculated. If less than or equal to ninety-five (95%) percent of thedata points from which the Normal RMS mean was calculated are more thanthe subject's RMS mean the associated "Score" component is accepted,otherwise it is rejected. Accepted "Score" component numbers are thenadded to provide a final numerical "Score".

It has been found that if said final numerical "Score" is "low", (eg.approximately 0 to 7), then the Subject involved is more likely to benormal. If the final numerical "Score" is "high", (eg. greater thanabout 8), then the Subject is more likely to be abnormal.

A particularly relevant approach to presenting the results of applyingthe disclosed method of the present invention is demonstrated by FIG. 5.FIG. 5 shows a plot in which the abscissa is (100-specificity) and theordinate is (sensitivity). These terms are well known and mean:##EQU41##

The curve in FIG. 5 is demonstrative of those which populations ofsubjects provide in an (ROC) format. The present invention methodprovides that (ROC) curves be prepared by associating a "Score" valuewith the abscissa, in a nonlinear manner, and the percentage of a grouphaving said "Score" value with the ordinate of such a plot. The successof the present invention in identifying and distinguishing abnormalsubjects has been demonstrated to be quite striking. FIGS. 8 and 9,discussed supra, better serve to demonstrate this with actualempirically derived data.

FIGS. 6X1 through 6Z4 show twelve (12) diagrams, 6X1, 6X2, 6X3, 6X4,6Y1, 6Y2, 6Y3, 6Y4, 6Z1, 6Z2, 6Z3 and 6Z4, of subject time domain sampledate recorded from Frank X, Y and Z leads. Higher frequency band dataare presented as one progresses from FIG. 6X1 to 6X4, and from FIG. 6Y1to 6Y4, and from FIG. 6Z1 to 6Z4.

More particularly, FIGS. 6X1, 6Y1 and 6Z1 show filtered compositesubject (ECG) data set time domain waveforms obtained from X, Y and Zleads, respectively, of a Frank ECG system in frequency band range of0.0-10 HZ. FIGS. 6X2, 6Y2 and 6Z2 show filtered composite subject (ECG)data set time domain waveforms obtained from X, Y and Z leads,respectively, of a Frank ECG system for the frequency band of 10-60 HZ.FIGS. 6X3, 6Y3 and 6Z3 show filtered composite subject (ECG) data settime domain waveforms obtained from X, Y and Z lead&, respectively, of aFrank ECG system for the frequency band of 60-150 HZ. FIGS. 6X4, 6Y4 and6Z4 show filtered composite subject (ECG) data set time domain waveformsobtained from X, Y and Z leads, respectively, of a Frank ECG system forthe frequency band of 150-250 HZ. It is noted that the plots for the60-150 and 150-250 HZ bands present as "envelopes" as signals gopositive to negative and vice versa in very short time periods, (i.e.over a few "Sample Numbers"). All plots in FIGS. 6X1-6Z4 have theordinate marked in micro-volts, and the abscissa is marked in digitalfilter data points 0 to 600, taken at progressive times during an (ECG)cycle.

It is noted that FIGS. 6Y3 and 6Y4 show "Rhomboids" present in thesegement past the QRS complex region, (ie. in channels 375-600). TheRhomboids are shown as dashed-line to indicate that they were added tothe actual patient data graph. This was done in preference to clutteringthe Disclosure with an additional page of Drawings, however, the pointto be made is that the presence of said "Rhomboids" in at least one timedomain, frequency band plot, and especially said presence in more thanone such frequency band plot, is very indicative of a patient in dangerof Sudden Death.

FIGS. 7aX1-7aZ5 show fifteen (15) diagrams of typical subject data infrequency domain Power Spectral Density form, (with Magnitude onordinate), plotted as a function of Frequency, (on abscissa). FIGS.7bX1-7bZ5 show fifteen (15) diagrams of typical subject data in TimeDomain form, (Magnitude on ordinate), plotted as a function of Time, (onabscissa). All said identified plots provide Magnitude, on the ordinate,in microvolts.

More particularly, it is noted that FIGS. 7aX1, 7aY1 and 7aZ1 showsubject composite (ECG) data set frequency domain power spectral densityplots derived from X, Y and Z leads, respectively, of a Frank ECGsystem, over a frequency band of 0.0 to 100 HZ. FIGS. 7aX2, 7aY2 and7aZ2 show subject composite (ECG) data set frequency domain powerspectral density plots derived from X, Y and Z leads, respectively, of aFrank ECG system, over a frequency band of 0.0 to 15 HZ. FIGS. 7aX3,7aY3 and 7aZ3 show subject composite (ECG) data set frequency domainpower spectral density plots derived from X, Y and Z leads,respectively, of a Frank ECG system, over a frequency band of 0.0 to 80HZ. FIGS. 7aX4, 7aY4 and 7aZ4 show subject composite (ECG) data setfrequency domain power spectral density plots derived from X, Y and Zleads, respectively, of a Frank ECG system, over a frequency band of 50to 200 HZ. FIGS. 7aX5, 7aY5 and 7aZ5 show subject composite (ECG) dataset frequency domain power spectral density plots derived from X, Y andZ leads, respectively, of a Frank ECG system, over a frequency band of100 to 300 HZ. All plots in FIGS. 7aX1-7aZ4 have the ordinate marked inmicro-volts, and the abscissa is marked in HZ, (i.e. cycles per second).

As well, FIGS. 7bX1, 7bY1 and 7bZ1 show subject composite (ECG) data settime domain waveforms obtained from X, Y and Z leads, respectively, of aFrank ECG system in an unfiltered full requisite frequency band. FIGS.7bX2, 7bY2 and 7bZ2 show subject composite (ECG) data set time domainwaveforms obtained from X, Y and Z leads, respectively, of a Frank ECGsystem in a filtered frequency band range of 0.0-10 HZ. FIGS. 7bX3, 7bY3and 7bZ3 show subject composite (ECG) data set time domain waveformsobtained from X, Y and Z leads, respectively, of a Frank ECG system in afiltered frequency band range of 10-60 HZ. FIGS. 7bX4, 7bY4 and 7bZ4show subject composite (ECG) data set time domain waveforms obtainedfrom X, Y and Z leads, respectively, of a Frank ECG system in a filteredfrequency band range of 60-150 HZ. FIGS. 7bX5, 7bY5 and 7bZ5 showsubject composite (ECG) data set time domain waveforms obtained from X,Y and Z leads, respectively, of a Frank ECG system in a filteredfrequency band range of 150-250 HZ. It is noted that the plots for the60-150 and 150-250 HZ bands present as "envelopes" as signals gopositive to negative aid vice versa in very short time periods, (ie.over a few "Sample Numbers"). All plots in FIGS. 7bX1-7bZ5 have theordinate marked in micro-volts, and the. abscissa is marked in digitalfilter Sample Number data points, taken at progressive times during a(ECG) cycle. The present invention then makes use of such visual aids asan added feature. The three curves in each plot represent normal meanand plus/minus one standard deviations, and subject data. It is also tobe understood that the above described approach to diagnosis can beapplied to tracking patients over time and can be applied before andafter various stress tests which attempt to provoke otherwise indolentor silent coronary artery abnormalities. Stress tests can, for example,involve treadmill exertion or a cold pressor test in which a subjectsimply places an arm into cold water for a few minutes. Changes in"Score" results combined with changes in the appearance of PowerSpectral Density (PSD) and Amplitude Plots over time or before and afterstress tests can provide insight as to a subject's coronary health notmade available by less vigerous testing. Multiple mean curves can besimultaneously presented on a single plot to allow easy visualcomparison of changes in Power Spectral Density as a function of time orstress. Observation of changes in (PSD) plots in the various frequencybands is a correlated part of the method of the present invention. Ofparticular interest, the inventor has noted that plots of (PSD) in thefrequency ranges of sixty (60) to one-hundred-fifty (150) HZ andone-hundred-fifty (150) HZ to two-hundred-fifty (250) HZ show thegreatest change in visually observable shape when a cold pressor test isadministered. This is considered a significant observation.

Note also that as shown in FIG. 7aX1, it is common to include numericalrepresentation in frequency as well as the time domain plots. Fournumbers can be present. Using the Power Spectral Density plot as anexample, when present said numbers are representations of:

Upper left--the number of Standard Deviations a Subject Power SpectralDensity Value is away from a corresponding Normal Power Spectral DensityValue for the Frequency Band in the Plot.

Lower Left--the Percentage of Normals which are below the Subject PowerSpectral Density Value for the Frequency Band in the Plot.

Upper Right--the number of Normalized, (ie. Subject Power SpectralDensity Value in the Frequency Band of the Plot divided by the Sum ofPower Spectral Density Values for all Frequency Bands), StandardDeviations a Subject Power Spectral Value is away from a correspondingNormalized Subject Power Spectral Density Value for Normals for theFrequency Band in the Plot.

Lower Right--the Percentage of Normals which are below the NormalizedSubject Power Spectral Density Value for the Frequency band in the Plot.

(Note that (RMS) values can be substituted for Power Spectral Density).Said numbers and visual Plots aid in interpretation of a Subject's"Score".

FIGS. 8 and 9 show (ROC) plots for actual data arrived at using thepresent invention method. Again, (ROC) curves typically plot Sensitivityvs. (100-Specificity) on ordinate and abscissa respectively, presentedas percentages. Said Plots in FIGS. 8 and 9 were generated byassociating the present invention "Score" with the abscissa(100-Specificity), but with the zero (0) thereof being at the right sideso that the "Score" increases to the left. As the "Score" increases thepercentage of each group of subjects associated therewith is plotted onthe ordinate. By observation of FIGS. 8 and 9 it will be appreciatedthat as the "Score" increases the percentage of normals in a group ofknown normals having said "Score" value drops off rapidly, but thepercentage of known abnormals in a group of known abnormals drops offmuch more slowly. For instance, at a "Score" of zero (0) all members ofall groups are present. At a "Score" of five (5) approximately eighty(80%) percent of all members of an abnormal group will be present, butonly approximately eleven (11%) percent of normals are present.

It is noted that a "Score" scale along the abscissa will be nonlinear,when compared to the (100-Specificity) scale.

FIG. 8 shows data presented in (ROC) format for Abnormals in variouscategories:

For subjects known to have had a myocardial infarction (MI) shown bytwelve (12) lead (ECG), identified as (BEMI);

For subjects with non-specific ST-T wave abnormality on twelve (12) lead(ECG), identified as (BEST);

For a subjects with normal resting twelve (12) lead (ECG) but awaitingsurgery, identified as (BNOB).

For a test set of patients who have (CAD), identified as (BTEST) and(BGENSIA).

FIG. 9 shows data plotted in FIG. 8 plotted in a different format inwhich the abcissa is scaled in terms of the "Score" developed by thepresent invention method.

Present is also a curve for Normals data, identified as (NORM).

Also included are curves for two groups additional groups of volunteersubjects which contain patients who have known risk factors for (CAD)identified as (BMAQ) and (BTNR). These constitute a "real-world"population of what are considered normals, in that both normals andabnormals are present. As would be expected, the data for the (BMAQ) and(BTNR) groups is generally positioned between the data for the knownabnormal (BTEST) and normal groups.

The important thing to note is that the method of the present inventionvery definitely separates the various groups whether presented in theformat of FIG. 8 or FIG. 9.

FIG. 102 provides a Flow Chart representation of the primary focus ofthe preferred embodiment of the Method of the present invention, saidmethod comprising a noninvasive approach to investigating cardiac statusof a subject, and enabling classification of a subject into normal andabnormal cardiac categories utilizing electrocardiography (ECG) dataobtained therefrom.

The first steps (A) and (A') respectively, are shown to involve:

a. in step (A) obtaining data corresponding to (ECG) cycle(s) from atleast one monitored lead(s) of an (ECG) system for a multiplicity ofmembers of a population of subjects who have been documented as normalsubjects, in that they do not show risk factors for, or demonstratedetectable cardiac abnormality; and

b. in step (A') obtaining data corresponding to (ECG) cycle(s) from atleast one monitored lead(s) of an (ECG) system for a subject, saidmonitored (ECG) system lead(s) utilized being the same as the monitored(ECG) system lead(s) utilized to obtain (ECG) cycle(s) for amultiplicity of normal subjects.

Next, in steps (B) and (B') respectively, a portion of an (ECG) cycle isselected, (eg. while the QRS complex is preferred, any portion, or theentire (ECG) cycle can be selected), for each of the normal subjectpopulation (B) and subject (B'). The same (ECG) cycle portion istypically selected for both the normal subject population and thesubject.

Next, in steps (C) and (C'), in conjunction with application offiltering techniques, a plurality of data sets are arrived at for eachmonitored (ECG) system lead for both the normal subject population,(step (C)), and for the subject, (step (C')). Each of said data setscorresponds to a composite of said selected (ECG) cycle portion for saidpopulation of normal subjects in a selected frequency band range. It isdisclosed that the preferred filtering technique is digital and has beenutilized to provide data sets for:

a. data contained in all frequencies;

b. data contained in the frequency band of (0.0) to (10) HZ;

c. data contained in the frequency band of (10) to (60) HZ;

d. data contained in the frequency band of (60) to (150) HZ;

e. data contained in the frequency band of (150) to (250) HZ.

It is specifically disclosed, however, that data in only one frequencyband range, (eg. all frequencies), can be provided in this step. It isalso noted that other frequency bands can be selected, and that therecited bands are exemplary and nonlimiting.

(It is noted that steps (A), (B), (C) and (E) might be carried out onlyonce for many runs of steps (A'), (B'), (C') and (E'). This is becausesteps (A), (B), (C) and (E) are applied to a normal subject population,which does not change, except perhaps when additional normal subjectdata are added to a normal subject populating data bank. Steps (A'),(B'), (C') and (E') must be run anew for each subject tested, however.Steps (D) and (F) will therefore access relatively standard values forthe normal subject population, while accessing new values for eachsubject tested. However, steps (A), (B), (C) and (E) are inherentlyperformed in the context of any testing of a subject.)

In step (D), normal subject population and subject (ECG) data set(s),(formed by user determined filtering techniques, (steps (C) and (C')),are plotted and displayed as a function of at least one parameterselected from the group consisting of time and frequency. It is notedthat where data is plotted as a function of frequency a time tofrequency domain conversion calculation must be performed to provide thefrequency domain data. The results of this step are to provide asdesired, visually interpretable plots of (ECG) magnitude and powerspectral density. (It is noted that variations of the present inventionprocedure provide that, this step can omitted, or be performed aftersteps which directly follow, (eg. steps (E) and (E') or step (F)). Step(D) is presented at this point in the flow chart only because the datato be plotted and displayed is available at this point. It is furthernoted that step (D) can be performed utilizing a different selected(ECG) cycle portion, in steps (B) and (B'), than is selected andutilized in following steps (E), (E') and (F)), (eg. a full (ECG) cyclecan be chosen for plotting and a QRS complex (ECG) cycle portion chosenfor use in steps (E), (E') and (F)).

The next steps, (E) and (E') respectively, involve calculatingcorresponding representative parameter(s), and desired ratios thereof,from resulting data sets in said selected frequency band ranges for eachmonitored (ECG) system lead, for both the normal subject population(step (E)) and said subject, (step (E')). These steps are indicated asperformed parallel to step (D) as data to allow both performance of step(D) and steps (E) and (E') is available at this point. (As noted above,the selected (ECG) cycle portion chosen in steps (B) and (B') andutilized in steps (E), (E') and (F) can be different than that utilizedin step (D). It is also to be understood that in one version of thepresent invention procedure, steps (E), (E') and (F) can be omitted andonly step (D) performed).

In step (F) corresponding subject and normal subject populationrepresentative parameter(s) and/or corresponding ratios of subject andratios of normal subject population representative parameters are thencompared and results of said comparison are combined to arrive at a"score", the magnitude of which "score" provides an indication of thecardiac status of said subject, and enables classification of a subjectinto normal and abnormal cardiac categories. A confidence level"acceptance test" can be optionally applied to qualify results of saidcomparisons for inclusion in arriving at said "score".

It is also noted that the step of calculating representativeparameter(s) for normal subject population and subject data sets formonitored (ECG) system leads typically involves calculating at least oneparameter selected from the group consisting of a root-mean-square meanand a root-mean-square standard deviation from said data set(s) fromwhich composite(s) of a selected (ECG) cycle portion are calculated.

It is further noted that obtaining mean and standard deviationparameters, (typically, but not necessarily, based upon root-mean-squarecalculated parameters), enables practice of an "acceptance test" whereina result of comparing subject to corresponding normal subject populationparameters or ratios of a subject to corresponding ratios of the normalsubject population parameters is accepted only if a subject acceptanceparameter is offset by greater than, for instance, at least one normalsubject population standard deviation from a mean of said normal subjectpopulation.

This step includes displaying of the "score" and when desired,components obtained from various composite data sets combined to arrivethereat.

FIGS. 10b, 10c, and 10d are additional flow charts which showmodifications of the method shown in FIG. 10a. The FIG. 10b Flow Chartshows application of the present invention method to Subject CardiacTracking. FIG. 10b is similar to FIG. 10a but the Normal SubjectPopulation of FIG. 10a is replaced with Initial Subject ECG Data, andthe Subject Data of FIG. 10a is replaced with Follow-On Subject Data.FIG. 10c shows application the present invention method of applyingConfidence Bands in Accepting or Rejecting a "Score Component"Representative Parameter in calculating a "Score". FIG. 10d is to beinterpreted to shows that after practicing the present invention methodsshown in the Flow Charts of FIGS. 10a and 10b, additional steps can beapplied which involve:

A. providing subject cardiac ejection fraction and dividing the "Score"thereby, and if the resulting value is over 1.0, considering saidsubject as at high risk for sudden death.

B. identification of Rhomboids in a portion of an ECG cycle which whichincludes at least a portion of the S-T segement following the QRScomplex, and if present, considering said subject as at high risk forsudden death; and

C. comparing "Score" Components values as shown in FIG. 12 to identifyPatterns therein, to further determine subject cardiac status, or changetherein.

It has further been found by investigation of CAMI/11 data base data forSubjects known to be at risk for Sudden Death, that if dividing apresent invention "Score" for a patient, (as provided by the describedpractice of the present invention), by the ejection fraction, (inPercent), of the patient, provides a result greater than one (1.0), thenthe patient involved is at high risk of Sudden Death.

A visual presentation of the just described phenomona is quite striking,as is shown by in FIGS. 11a and 11b which show scatter-graphsdemonstrating the relationship between said present invention:

("Score"/Ejection Fraction)

plotted against the present invention "Score", (termed "SEECAD"™ Score.("SEECAD" is a Trademark owned by R & S Incorporated, a CanadianCorporation). Note, as shown in FIG. 11b, that a Population of Subjectsnot at risk for Sudden Death present with results wherein Subject data"scatter" is closely confined about the line which begins at (0.0, 0.0)and ends at (50, 1.5); whereas a population which demonstrated SuddenDeath presents with data which demonstrate a much larger range ofscatter. In addition, and most importantly on an individual patientbasis, note that no Subject data in FIG. 11b exceed the value of 1.0 onthe Abscissa, whereas a large number of Subjects shown in FIG. 11aprovide data points above 1.0. The bottom line conclusion to beappreciated is that should a Subject present with a:

("Score"/Ejection Fraction)

value greater than 1.0, said Subject should be considered to definitelybe a risk for Sudden Death. If said ratio is coupled with the presenceof previously described "Rhomboids" present following a QRS complex inTime Domain Plots, (see for instance demonstration in FIGS. 6Y3 and6Y4), then the patient involved should be considered to be at very highrisk for sudden death. This combination of present invention "SEECAD"Score with other typically obtained Cardiac Data provides insight to thepotential scope of application of the present invention. The definitionof and availability of the described "SEECAD" Score provided by practiceof the present invention, has opened a whole new and very promisingavenue in the area of Subject evaluation.

FIG. 12 shows a three-dimensional presentation of Data Componentsutilized in computing a "SEECAD"™ Score. FIG. 12 is included to showthat such a presentation indicates that Patterns of:

Data components Standard Deviation from Normality,

which data components were derived utilizing present inventionmethodology, can identify specific Subject Abnormality Data Patterns. Itis emphasized that known efforts of previous researchers have had as afocus the diagnosis of Subject abnormality by the comparison of:

Subject Data to Abnormal Subject Population Data, and looking for amatch.

The present invention then has a new focus, emphasis added. Again, thepresent invention focus is on comparing Subject Data to Normal SubjectPopulation Data, and Patterns of Data Components which naturally arisethereform are found to be indicative of Specific Catagories ofAbnormality. The fact that the present invention approach, based incomparing Subject Data to Normal Subject Population Data, results inData Patterns which serve to indicate a Specific Subject Abnormality isa distinguishing factor of the present invention, and provides anextremely exciting area of continued development. It is projected thatfurther work utilizing present invention non-invasively obtained"SEECAD" Score data and methodology will provide the ability to not onlyseparate Abnormal from Normal Subjects, (already possible), but tofurther identify the most likely anatomical location of the source ofidentified Abnormality, (eg. specific myocardium, specificcoronary-arteries etc).

FIG. 13 shows a Diagram of the basic components of a system which can beutilized to practice the present invention method. A partial human torsois shown with a Chest mounted Bioelectric Interface (BI) thereon. (Notethat equivalent limb electrodes (RA), (LA) (LL) are present therein).Conventional individual Limb, (e.g. RA', LA' and LL') and Precordial(ie. (v1), (v2), (v3), (v4), (v5) and (v6)) leads can, of course, beutilized as well, and shown precordial leads (V1), (V2), (V3), (V4),(V5) and (V6) are to be interpreted sufficiently broadly to indicateindividual electrodes are accessed on the shown subject upper torso. Ithas been found, however, that use of a chest mountable BioelectricInterface (BI), as shown, provides better (ECG) signals by maintainingrelatively better electrode contact to a subject and relatively constantelectrode spacing, in use. A Cable (C) provides electrical signals fromsaid electrodes (RA), (LA), (LL,) (v1), (v2), (v3), (v4), (v5) and (v6)to an (ECG) monitor (ECG), which feeds to a Computational Means(COMPUTER), which in turn provides SEECAD™ data to a (VISUAL DISPLAY)and to a (PRINTER/PLOTTER). Of course FIG. 13 is only demonstrative andthe present invention system is not limited to the configuration shown.

It is to be understood that throughout this Disclosure the RMS Meanvalues are cited. It is possible to utilize other calculated values,such as Averages, in the method of the present invention. The term"Mean" should be interpreted broadly to include such alternatives.

The terms "Assumed Gaussian" have also been used throughtout thisDisclosure when refering to Data Distribution RMS Means and RMS StandardDeviations. It is noted that in fact, analysis of empirically obtaineddata has proven the assumption to be valid.

It is also to be understood that the Term "Rhomboid" is used herein onlyto generally identify the presence of (ECG) activity beyond the QRScomplex as shown by dashed lines in FIGS. 6Y3 and 6Y4, and does notimpose any plot locus shape limitations.

To provide full disclosure a print-out of major portions of the computerprogram utilized in the practice of the present invention is includedherewith directly. Also included following the computer print-out is atable of data which documents the above discussed results. ##SPC1##

It is also disclosed that Tracking of a subject can be continuous, andcan utilize data obtained before and after, for instance: a suitablestress test; intervention (angioplasty etc.); and/or medical therapy. Ofinterest is the fact that signal magnitude in Frequency Domain Plots,(eg. 7aX1-7aZ5), particulary in 60-150 and 150-250 HZ ranges hasroutinely been noted to drop by thirty (30%) percent or more uponsubjecting patients who are prone to ischemia, to a cold-pressor test.

It is also to be understood that the terminology "Coronary ArteryDisease" is used throughout this Disclosure, the present inventionserves to identify Coronary Disfunction generally, which can includemyocaridal poblems separate from Coronary Artery disease per se.

Finally, it is generally described herein that, for instance, asdifferences between Normal Subject Population, and SubjectRepresentative Parameters increase, the "Score" of the present inventionincreases. It would be a simple matter indeed to place a negative signon the "Score" and declare that it "decreases" when differences betweenNormal Subject Population and Subject Representative Parametersincrease. It would further be a simple matter to utilize slightlydifferent but substantially the same Normal Subject Population, andSubject Data Frequency Bands, or select slightly different butsubstantially the same Normal Subject Population, and Subject Data (ECG)cycle portions. As to attempt to draft definite claim language toovercome all such possibilities would be an impossible task in view ofthe complexity of the present invention subject matter, it is thereforeto be understood that the Doctrine of Equivalent applies to, and theclaims are to be interpreted to include all such contrived andsubstantially indifferent functional equivalents in the practice of therecited method of the present invention, emphasis added.

Having hereby disclosed the subject matter of the present invention, itshould be obvious that many modifications, substitutions and variationsof the present invention are possible in light of the teachings. It istherefore to be understood that the invention may be practiced otherthan as specifically described, and should be limited in breadth andscope only by the claims.

I claim:
 1. An ECG system for practicing a noninvasive method ofinvestigating cardiac status of a subject and enabling classification ofsaid subject into normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom, said method comprising,in a functional sequence, performance of the steps of:a. obtaining datafrom ECG cycle(s) from each of a multiplicity of members of a populationof subjects who have been documented as normal subjects, in that they donot show risk factors for, or demonstrate detectable cardiacabnormality, by providing, selecting and monitoring at least one lead(s)of said ECG system; b. establishing criteria for, and in line therewithselecting some ECG cycle portion and defining cycle portion data pointstherewithin, and calculating an average selected ECG cycle portion dataset for said at least one monitored ECG system lead(s) by, for amonitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from each of a number of members ofsaid multiplicity of members of a population of subjects who have beendocumented as normal subjects, each said calculated average selected ECGcycle portion data set being a composite data set of said selected ECGcycle portion for said population of normal subjects, for a monitoredECG system lead; c. obtaining data from ECG cycle(s) from a subject, bymonitoring at least one lead(s) of said ECG system, said ECG systemlead(s) monitored being the same as the monitored ECG system lead(s)utilized in step a. to obtain data utilized in step b.; d. selectingsome ECG cycle portion, which is essentially that selected in step b.,and calculating an average selected ECG cycle portion data set for saidat least one monitored ECG system lead(s) by, for a monitored ECG systemlead, a procedure comprising combining corresponding ECG cycle portiondata points for said selected ECG cycle portion for ECG cycle(s)obtained from said subject, each said calculated average selected ECGcycle portion data set being a composite data set of said selected ECGcycle portion for said subject, for a monitored ECG system lead; e.calculating corresponding representative parameter(s) from resultingcomposite data sets calculated in steps b. and d., for monitored ECGsystem lead(s), for, respectively, said normal subject population andsaid subject; f. comparing subject to corresponding normal subjectpopulation representative parameter(s), and combining results thereof toarrive at a "score", the magnitude of which "score" results fromdifference(s) between magnitude(s) of corresponding normal subjectpopulation, and subject representative parameter(s), which "score"magnitude increases when said difference(s) in magnitude(s) betweencorresponding normal subject population, and subject, representativeparameter(s) increase, the magnitude of which "score" provides anindication of the cardiac status of said subject, with a "score" nearzero being indicative of a subject properly categorized as a cardiacnormal in that the magnitude(s) of subject representative parameter(s)are generally more closely matched to the magnitude(s) of correspondingnormal subject population representative parameter(s), and with aprogressively higher "score" being indicative of a subject progressivelymore properly categorized as a cardiac abnormal in that the magnitude(s)of subject representative parameter(s) are generally progressively lessclosely matched to the magnitude(s) of corresponding normal subjectpopulation representative parameter(s); g. providing an output means forpresenting said score;said ECG system comprising ECG lead(s) whichmonitor electrodes affixed to a subject or member of a normal subjectpopulation, and which provide monitored signal(s) to an ECG monitor,said ECG monitor being functionally interconnected to a computationalmeans which is programmed to accept ECG data from said ECG monitor andpractice the method of steps a.-f., said computational means beingfunctionally interconnected to an output means to enable practice ofstep g.
 2. A system for practicing a noninvasive method of investigatingcardiac status of a subject and enabling classification of a subjectinto normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom as in claim 1, whichmethod further comprises performance of the additional steps ofcalculating, and comparing ratio(s) of subject, to correspondingratio(s) of normal subject population, representative parameters, andcombining results thereof with those from comparing subject tocorresponding normal subject population, representative parameter(s), inarriving at said "score", the magnitude of which "score" then furtherresults from difference(s) between magnitude(s) of corresponding normalsubject population and subject ratio(s) of representative parameters,which "score" magnitude increases when difference(s) in magnitude(s)between corresponding normal subject population, and subject, ratio(s)of representative parameters increase;said ECG system computationalmeans being programmed to perform said additional steps.
 3. An ECGsystem for practicing a noninvasive method of investigating cardiacstatus of a subject and enabling classification of said subject intonormal and abnormal cardiac categories utilizing electrocardiography ECGdata obtained therefrom as in claim 1, which method further comprisesperformance of the additional steps of:a. providing at least onecoordinate system selected from the group consisting of magnitude vs.time, and magnitude vs. frequency; b. for an ECG cycle portion,performing calculations necessary to plot and display and plotting anddisplaying normal subject population and subject ECG data as a functionof at least one parameter selected from the group consisting of time andfrequency, to respectively provide as desired, visually interpretableplots of ECG magnitude and power spectral density data, observation ofwhich provides an indication of the cardiac status of said subject; andc. observing and interpreting the displayed plot(s);said ECG systemcomputational means being programmed to perform said additional step andcause display thereof via output display means.
 4. An ECG system forpracticing a noninvasive method of investigating cardiac status of asubject and enabling classification of said subject into normal andabnormal cardiac categories utilizing electrocardiography ECG dataobtained therefrom, said method comprising, in a functional sequence,performance of the steps of:a. obtaining data from ECG cycle(s) fromeach of a multiplicity of members of a population of subjects who havebeen documented as normal subjects, in that they do not show riskfactors for, or demonstrate detectable cardiac abnormality, byproviding, selecting and monitoring at least one lead(s) of said ECGsystem; b. establishing criteria for, and in line therewith selectingsome ECG cycle portion, and defining cycle portion data pointstherewithin, and calculating an average selected ECG cycle portion dataset for at least one monitored ECG system lead(s) by, for a monitoredECG system lead, a procedure comprising combining corresponding ECGcycle portion data points for said selected ECG cycle portion for ECGcycle(s) obtained from each of a number of members of said multiplicityof members of a population of subjects who have been documented asnormal subjects, each said calculated average selected ECG cycle portiondata set being a composite data set of said selected ECG cycle portionfor said population of normal subjects, for a monitored ECG system lead;c. obtaining data from ECG cycle(s) from a subject, by monitoring atleast one lead(s) of ECG said system, said ECG system lead(s) monitoredbeing the same as the monitored ECG system lead(s) utilized in step a.to obtain data utilized in step b.; d. selecting some ECG cycle portion,which is essentially that selected in step b., and calculating anaverage selected ECG cycle portion data set for said at least onemonitored ECG system lead(s) by, for a monitored ECG system lead, aprocedure comprising combining corresponding ECG cycle portion datapoints for said selected cycle portion for ECG cycle(s) obtained fromsaid subject, each said calculated average selected ECG cycle portiondata set being a composite data set of said selected ECG cycle portionfor said subject, for a monitored ECG system lead; e. calculatingcorresponding representative parameter(s) and corresponding ratio(s)involving representative parameters from resulting composite data setscalculated in steps b. and d., for monitored ECG system lead(s), for,respectively, said normal subject population and said subject; f.comparing specific ratio(s) of subject to corresponding specificratio(s) of normal subject population representative parameters, andcombining results thereof to arrive at a "score", the magnitude of which"score" results from difference(s) between magnitude(s) of specificcorresponding ratio(s) of normal subject population, and ratio(s) ofsubject representative parameters, which "score" magnitude increaseswhen said difference(s) in magnitude(s) between specific ratio(s) ofcorresponding normal subject population, and specific ratio(s) ofsubject representative parameters increase, the magnitude of which"score" provides an indication of the cardiac status of said subject,with a "score" near zero being indicative of a subject properlycategorized as a cardiac normal in that the magnitude(s) of ratio(s) ofsubject representative parameters are generally more closely matched tothe magnitude(s) of corresponding ratio(s) of normal subject populationrepresentative parameters, and with a progressively higher "score" beingindicative of a subject progressively more properly categorized as acardiac abnormal in that the magnitude(s) of ratio(s) of subjectrepresentative parameters are generally progressively less closelymatched to the magnitude(s) of ratio(s) of corresponding normal subjectpopulation representative parameters; g. providing an output means forpresenting said score;said ECG system comprising ECG lead(s) whichmonitor electrodes affixed to a subject or member of a normal subjectpopulation, and which provide monitored signal(s) to an ECG monitor,said ECG monitor being functionally interconnected to a computationalmeans which is programmed to accent ECG data from said ECG monitor andpractice the method of steps a.-f. said computational means beingfunctionally interconnected to an output means to enable practice ofstep g.
 5. An ECG system for practicing a noninvasive method ofinvestigating cardiac status of a subject and enabling classification ofsaid subject into normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom as in claim 7, whichmethod further comprises performance of the additional steps of:a.providing at least one coordinate system selected from the groupconsisting of magnitude vs. time, and magnitude vs. frequency; b. for anECG cycle portion, performing calculations necessary to plot and displayand plotting and displaying normal subject population and subject ECGdata, as a function of at least one parameter selected from the groupconsisting of time and frequency, to respectively provide as desired,visually interpretable plots of ECG magnitude and power spectral densitydata, observation of which provides an indication of the cardiac statusof said subject; and c. observing and interpreting displayedplot(s);said ECG system computational means being programmed to performsaid additional step and cause display thereof via output display means.6. An ECG system for practicing a noninvasive method of investigatingcardiac status of a subject and enabling classification of said subjectinto normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom, said method comprising,in a functional sequence, performance of the steps of:a. obtaining datafrom ECG cycle(s) from each of a multiplicity of members of a populationof subjects who have been documented as normal subjects, in that they donot show risk factors for, or demonstrate detectable cardiacabnormality, by providing, selecting and monitoring at least one lead(s)of an ECG system; b. establishing criteria for, and in line therewithselecting some ECG cycle portion and defining cycle portion data pointstherewithin, and calculating an average selected ECG cycle portion dataset for said at least one monitored ECG system lead(s), by, for amonitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from each of a number of saidmultiplicity of members of a population of subjects who have beendocumented as normal subjects, and selecting a plurality of frequencybands and applying filtering techniques, to provide a plurality of datasets for each said at least one ECG system lead(s) monitored, each saiddata set being a composite data set of said selected ECG cycle portionfor said population of normal subjects in a monitored lead and selectedfrequency band range; c. obtaining data from ECG cycle(s) from asubject, by monitoring at least one lead(s) of said ECG system, said ECGsystem lead(s) monitored being the same as the monitored ECG systemlead(s) utilized in step a. to obtain data utilized in step b.; d.selecting some ECG cycle portion, said ECG cycle portion beingessentially that selected in step b. for said normal subject population,and calculating an average selected ECG cycle portion data set for saidat least one monitored ECG system lead(s), by, for a monitored ECGsystem lead, a procedure comprising combining corresponding ECG cycleportion data points for said selected ECG cycle portion for subject ECGcycle(s), and selecting a plurality of frequency bands, said selectedfrequency bands being essentially those selected in step b. for saidnormal subject population, and applying filtering techniques which areessentially those applied in step b. for said normal subject population,to provide a plurality of data sets for each said at least one monitoredECG system lead(s), each said data set being a composite data set ofsaid selected ECG cycle portion for said subject in a monitored lead andselected frequency band range; p1 e. calculating correspondingrepresentative parameter(s) from resulting composite data setscalculated in steps b. and d., in said selected frequency band rangesfor monitored ECG system lead(s), for respectively, said normal subjectpopulation and said subject; f. comparing specific subject to specificnormal subject population corresponding representative parameter(s), andcombining results thereof to arrive at a "score", the magnitude of which"score" results from difference(s) between magnitude(s) of correspondingnormal subject population and subject representative parameter(s), which"score" magnitude increases when said difference(s) in magnitude(s)between corresponding normal subject population and subjectrepresentative parameter(s) increase, the magnitude of which "score"provides an indication of the cardiac status of said subject, with a"score" near zero being indicative of a subject properly categorized asa cardiac normal in that the magnitude(s) of subject representativeparameter(s) are generally more closely matched to the magnitude(s) ofcorresponding normal subject population representative parameter(s), andwith a progressively higher "score" being indicative of a subjectprogressively more properly categorized as a cardiac abnormal in thatthe magnitude(s) of subject representative parameter(s) are generallyprogressively less closely matched to the magnitude(s) of correspondingnormal subject population representative parameter(s); g. providing anoutput means for presenting said score;said ECG system comprising ECGlead(s) which monitor electrodes affixed to a subject or member of anormal subject population, and which provide monitored signal(s) to anECG monitor, said ECG monitor being functionally interconnected to acomputational means which is programmed to accept ECG data from said ECGmonitor and practice the method of steps a.-f., said computational meansbeing functionally interconnected to an output means to enable practiceof step g.
 7. An ECG system for practicing a noninvasive method ofinvestigating cardiac status of a subject and enabling classification ofsaid subject into normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom as in claim 6, whichmethod further comprises performance of the steps of:a. providing atleast one coordinate system selected from the group consisting ofmagnitude vs. time, and magnitude vs. frequency; b. for an ECG cycleportion, performing calculations necessary to plot and display andplotting and displaying normal subject population and subject ECG datain at least one frequency band range, as a function of at least oneparameter selected from the group consisting of time and frequency, torespectively provide as desired, visually interpretable plots of ECGmagnitude and power spectral density data, observation of which providesan indication of the cardiac status of said subject; and c. observingand interpreting displayed plot(s);said ECG system computational meansbeing programmed to perform said additional step and cause displaythereof via output display means.
 8. An ECG system for practicing anoninvasive method of investigating cardiac status of a subject andenabling classification of said subject into normal and abnormal cardiaccategories utilizing electrocardiography ECG data obtained therefrom,said method comprising, in a functional sequence, performance of thesteps of:a. obtaining data from ECG cycle(s) from each of a multiplicityof members of a population of subjects who have been documented asnormal subjects, in that they do not show risk factors for, ordemonstrate detectable cardiac abnormality, by providing, selecting andmonitoring at least one lead(s) of an ECG system; b. establishingcriteria for, and in line therewith selecting some ECG cycle portion anddefining cycle portion data points therewithin, and calculating anaverage selected ECG cycle portion data set for at least one monitoredECG system lead(s), by, for a monitored ECG system lead, a procedurecomprising combining corresponding ECG cycle portion data points forsaid selected ECG cycle portion for ECG cycle(s) obtained from each of anumber of said multiplicity of members of a population of subjects whohave been documented as normal subjects, and selecting a plurality offrequency bands and applying filtering techniques, to provide aplurality of data sets for said at least one ECG system lead(s)monitored, each said data set being a composite data set of saidselected ECG cycle portion for said population of normal subjects in amonitored lead and selected frequency band range; c. obtaining data fromECG cycle(s) from a subject, by monitoring at least one lead(s) of saidECG system, said ECG system lead(s) monitored being the same as themonitored ECG system lead(s) utilized in step a. to obtain data utilizedin step b.; d. selecting some ECG cycle portion, said ECG cycle portionbeing essentially that selected in step b. for said normal subjectpopulation, and calculating an average selected ECG cycle portion dataset for said at least one monitored ECG system lead(s), by, for amonitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for subject ECG cycle(s), are selecting a plurality of frequencybands, said selected frequency bands being essentially those selected instep b. for said normal subject population, and applying filteringtechniques which are essentially those applied in step b. for saidnormal subject population, to provide a plurality of data sets for saidat least one monitored ECG system lead(s), each said data set being acomposite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. calculatingcorresponding representative parameter(s) and corresponding ratio(s)involving representative parameters from resulting composite data setscalculated in steps b. and d., in said selected frequency band rangesfor monitored ECG system lead(s), for respectively, said normal subjectpopulation and said subject; f. comparing specific ratio(s) of subjectto corresponding specific ratio(s) of normal subject populationrepresentative parameters, and combining results thereof to arrive at a"score", the magnitude of which "score" results from difference(s)between magnitude(s) of corresponding specific ratio(s) of normalsubject population and specific ratio(s) of subject representativeparameters, which "score" magnitude increases when said difference(s) inmagnitude(s) between specific ratio(s) of corresponding normal subjectpopulation and subject representative parameters increase, the magnitudeof which "score" provides an indication of the cardiac status of saidsubject, with a "score" near zero being indicative of a subject properlycategorized as a cardiac normal in that the magnitude(s) of ratio(s) ofsubject representative parameters are generally more closely matched tothe magnitude(s) of corresponding ratio(s) of normal subject populationrepresentative parameters, and with a progressively higher "score" beingindicative of a subject progressively more properly categorized as acardiac abnormal in that the magnitude(s) of ratio(s) of subjectrepresentative parameters are generally progressively less closelymatched to the magnitude(s) of ratio(s) of corresponding normal subjectpopulation representative parameters; g. providing an output means forpresenting said score;said ECG system comprising ECG lead(s) whichmonitor electrodes affixed to a subject or member of a normal subjectpopulation, and which provide monitored signal(s) to an ECG monitor,said ECG monitor being functionally interconnected to a computationalmeans which is programmed to accept ECG data from said ECG monitor andpractice the method of steps a.-f., said computational means beingfunctionally interconnected to an output means to enable practice ofstep g.
 9. An ECG system for practicing a noninvasive method ofinvestigating cardiac status of a subject and enabling classification ofsaid subject into normal and abnormal cardiac categories utilizingelectrocardiography ECG data obtained therefrom as in claim 8, whichmethod further comprises performance of the steps of:a. providing atleast one coordinate system selected from the group consisting ofmagnitude vs. time, and magnitude vs. frequency; b. for an ECG cycleportion, performing calculations necessary to plot and display andplotting and displaying normal subject population and subject ECG datafor at least one frequency band range, as a function of at least oneparameter selected from the group consisting of time and frequency, torespectively provide as desired, visually interpretable plots of ECGmagnitude and power spectral density data, observation of which providesan indication of the cardiac status of said subject; and c. observingand interpreting displayed plot(s);said ECG system computational meansbeing programmed to perform said additional step and cause displaythereof via output display means.
 10. An ECG system for practicing anoninvasive method of investigating cardiac status of a subject andenabling classification of said subject into normal and abnormal cardiaccategories utilizing electrocardiography ECG data obtained therefrom,said method comprising, in a functional sequence, performance of thesteps of:a. obtaining data from ECG cycle(s) from each of a multiplicityof members of a population of subjects who have been documented asnormal subjects, in that they do not show risk factors for, ordemonstrate detectable cardiac abnormality, by providing, selecting andmonitoring at least one lead(s) of an ECG system; b. establishingcriteria for, and in line therewith selecting some ECG cycle portion anddefining cycle portion data points therewithin, and calculating anaverage selected ECG cycle portion data set for said at least onemonitored ECG system lead(s), by, for a monitored ECG system lead, aprocedure comprising combining corresponding ECG cycle portion datapoints for said selected ECG cycle portion for ECG cycle(s) obtainedfrom each of a number of said multiplicity of members of a population ofsubjects who have been documented as normal subjects, and selecting aplurality of frequency bands and applying filtering techniques, toprovide a plurality of data sets for said at least one ECG systemlead(s) monitored, each said data set being a composite data set of saidselected ECG cycle portion for said population of normal subjects in amonitored lead and selected frequency band range; c. obtaining data fromECG cycle(s) from a subject, by monitoring at least one lead(s) of saidECG system, said ECG system lead(s) monitored being the same as themonitored ECG system lead(s) utilized in step a. to obtain data utilizedin step b.; d. selecting some ECG cycle portion, said ECG cycle portionbeing essentially that selected in step b. for said normal subjectpopulation, and calculating an average selected ECG cycle portion dataset for said at least one monitored ECG system lead(s), by, for amonitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for subject ECG cycle(s), and selecting a plurality of frequencybands, said selected frequency bands being essentially those selected instep b. for said normal subject population, and applying filteringtechniques which are essentially those applied in step b. for saidnormal subject population, to provide a plurality of data sets for saidat least one monitored ECG system lead(s), each said data set being acomposite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. calculatingcorresponding representative parameter(s) and corresponding ratio(s)involving representative parameters from resulting composite data setscalculated in steps b. and d., in said selected frequency band rangesfor monitored ECG system lead(s), for respectively, said normal subjectpopulation and said subject; f. comparing specific subject andcorresponding specific normal subject population representativeparameter(s), and combining results thereof with the results ofcomparing specific ratio(s) of subject to corresponding specificratio(s) of normal subject population representative parameters, toarrive at a "score", the magnitude of which "score" results fromdifference(s) in magnitude(s) between corresponding subject and normalsubject population representative parameter(s) and difference(s) betweenmagnitude(s) of corresponding ratio(s) of normal subject population, andratio(s) of subject representative parameters, which "score" magnitudeincreases when difference(s) in magnitude(s) between correspondingsubject and normal subject population representative parameter(s)increase and difference(s) in magnitude(s) between ratio(s) ofcorresponding normal subject population, and ratio(s) of subjectrepresentative parameters increase, the magnitude of which "score"provides an indication of the cardiac status of said subject, with a"score" near zero being indicative of a subject properly categorized asa cardiac normal in that magnitude(s) of subject representativeparameter(s) are generally more closely matched to the magnitude(s) ofcorresponding normal subject population representative parameter(s) andmagnitude(s) of ratio(s) of subject representative parameters aregenerally more closely matched to the magnitude(s) of correspondingratio(s) of normal subject population representative parameters, andwith a progressively higher "score" being indicative of a subjectprogressively more properly categorized as a cardiac abnormal in thatmagnitude(s) of subject representative parameter(s) are generallyprogressively less closely matched to the magnitude(s) of correspondingnormal subject population representative parameter(s) and magnitude(s)of ratio(s) of subject representative parameter(s) are generallyprogressively less closely matched to the magnitude(s) of ratio(s) ofcorresponding normal subject population representative parameters; g.providing an output means for presenting said score;said ECG systemcomprising ECG lead(s) which monitor electrodes affixed to a subject ormember of a normal subject population, and which provide monitoredsignal(s) to an ECG monitor, said ECG monitor being functionallyinterconnected to a computational means which is programmed to acceptECG data from said ECG monitor and practice the method of steps a.-f.,said computational means being functionally interconnected to an outputmeans to enable practice of step g.
 11. An ECG system for practicing anoninvasive method of investigating cardiac status of a subject andenabling classification of said subject into normal and abnormal cardiaccategories utilizing electrocardiography ECG data obtained therefrom asin claim 10, which method further comprises performance of the stepsof:a. providing at least one coordinate system selected from the groupconsisting of magnitude vs. time, and magnitude vs. frequency; b. for anECG cycle portion, performing calculations necessary to plot and displayand plotting and displaying normal subject population and subject ECGdata for at least one frequency band range, as a function of at leastone parameter selected from the group consisting of time and frequency,to respectively provide as desired, visually interpretable plots of ECGmagnitude and power spectral density data, observation of which providesan indication of the cardiac status of said subject; and c. observingand interpreting displayed plot(s);said ECG system computational meansbeing programmed to perform said additional step and cause displaythereof via output display means.
 12. A noninvasive method of trackingcardiac status of a subject utilizing electrocardiography ECG dataobtained therefrom, said method comprising, in a functional sequence,performance of the steps of:a. obtaining initial data from ECG cyclesfrom a subject, by providing, selecting and monitoring at least onelead(s) of an ECG system; b. establishing criteria for, and in linetherewith selecting some ECG cycle portion and defining cycle portiondata points therewithin, and calculating an average selected ECG cycleportion data set for said at least one monitored ECG system lead(s), by,for a monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from said subject, and selecting aplurality of frequency bands and applying filtering techniques, toprovide a plurality of data sets for said at least one ECG systemlead(s) monitored, each said data set being an initial composite dataset of said selected ECG cycle portion for said subjects in a monitoredlead and selected frequency band range; c. obtaining follow-on data fromECG cycle(s) from a subject at a later time, by monitoring said at leastone lead(s) of an ECG system, said ECG system lead(s) monitored beingthe same as the monitored ECG system lead(s) utilized in step a. toobtain data utilized in step b.; d. selecting some ECG cycle portion,said ECG cycle portion being essentially that selected in step b. forsaid initial subject data, and calculating an average selected ECG cycleportion data set for at least one monitored ECG system lead(s), by, fora monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for subject ECG cycle(s), and selecting a plurality of frequencybands, said selected frequency bands being essentially those selected instep b. for said initial subject data, and applying filtering techniqueswhich are essentially those applied in step b. for said initial subjectdata, to provide a plurality of data sets for said at least onemonitored ECG system lead(s), each said data set being a follow-oncomposite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. calculatingcorresponding representative parameter(s) from resulting composite datasets calculated in steps b. and d., in said selected frequency bandranges for monitored ECG system lead(s), for respectively, said initialsubject data and said follow-on subject data; f. comparing values for atleast one member of the group consisting of:initial subject tocorresponding follow-on subject representative parameter(s), andspecific ratio(s) of initial subject to corresponding specific ratio(s)of follow-on subject representative parameters,and combining resultsthereof to arrive at a "score"; the magnitude of which "score" resultsfrom difference(s) between magnitude(s) of corresponding initialsubject, and follow-on subject representative parameter(s) and/orratio(s) of initial subject representative parameters, and follow-onsubject representative parameters; which "score" magnitude increaseswhen said difference(s) in magnitude(s) between corresponding initialsubject, and follow-on subject, representative parameter(s) and/orratio(s) of initial subject representative parameters, and follow-onsubject representative parameters increase, the magnitude of which"score" provides an indication of a change in cardiac status of saidsubject, with a "score" near zero being indicative of a subject properlycategorized as having undergone no cardiac change, and with aprogressively higher "score" being indicative of a subject progressivelymore properly categorized as a subject who has undergone cardiac change;and g. providing an output means and presenting said scoretherewith;said method optionally further comprising as additionalstep(s) groupings of steps selected from the group consisting of: h., i,and j; k., l, and m; and n and o.;said steps h., i., and j., being: h.determining the subject's cardiac ejection fraction, (in percent); i.dividing said "score" determined in step f. by said cardiac ejectionfraction, (in percent); j. providing an output means and presenting theresult provided in step i. therewith, and if said result is determinedto be greater than one (1.0), considering said subject as at high riskfor sudden death;and said steps k., l. and m. being: k. providing atleast a coordinate system consisting of magnitude vs. time, andoptionally a coordinate system consisting of magnitude vs. frequency,and in step b. selecting a portion of the ECG cycle including the regionbeyond the QRS complex and before the T wave; l. for said ECG cycleportion, performing calculations necessary to plot and display initialsubject and follow-on subject ECG data as a function of at least timeand optionally frequency, to respectively provide as desired, visuallyinterpretable plots of ECG magnitude and power spectral density data,observation of which provides an indication of the cardiac status ofsaid subject; and m. providing an output display means and visuallyplotting and displaying therewith at least a magnitude vs. time plot forsaid ECG cycle portion beyond the QRS complex and before the T wave,then noting if Rhomboids are therewithin, and if present, consideringsaid subject as at high risk for sudden death; andsaid steps n. and o.being: n. determining realtive magnitude pattern(s) amongst at least oneselection from the group consisting of:subject represenatative parametervalues; and ratios of subject representative parameter values; and o.providing an output means, and via said output means obtaining andutilizing said relative magnitude pattern(s) as additional basis fortracking said subject cardiac status.
 13. A noninvasive method oftracking the cardiac status of a subject utilizing electrocardiographyECG data obtained therefrom as in claim 12, which further comprisesperformance of the step of applying a confidence level acceptance testto results of comparing corresponding specific initial subject tospecific follow-on subject representative parameters, and/orcorresponding specific ratios of said representative parameters offollow-on subject, to specific ratios of initial normal subject,representative parameters prior to combining results thereof to arriveat a "score", said confidence level test comprising:a. the step ofdetermining mean and standard deviation acceptance parameters forspecific initial subject representative parameter(s) and/or specificratio(s) of initial subject representative parameter(s) based upon theECG data from which said composite data set of said selected ECG cycleportion for said initial subject data was calculated; b. the step ofdetermining an acceptance parameter for each specific correspondingfollow-on subject representative parameter and/or each correspondingspecific ratio of follow-on subject representative parameters based uponthe ECG data from which said composite data set of said selected ECGcycle portion for said follow-on subject data was calculated; and c. thestep of accepting the results of comparing a specific follow-on subjectrepresentative parameter to a corresponding specific initial subjectrepresentative parameter in arriving at said "score", only if theacceptance parameter for said specific follow-on subject representativeparameter is set off by at least one associated initial subjectacceptance standard deviation from the acceptance mean of thecorresponding specific initial subject representative parameter; and/oraccepting the results of comparing a specific ratio of follow-on subjectrepresentative parameters to a corresponding specific ratio ofrepresentative parameters for said initial subject population, inarriving at said "score", only if the acceptance parameter of saidspecific ratio of said follow-on subject representative parameters isset off by at least one associated initial subject acceptance standarddeviation from the acceptance mean of said corresponding specific ratioof initial subject representative parameters.
 14. A noninvasive methodof tracking cardiac status change in a subject utilizingelectrocardiography ECG data obtained therefrom as in claim 12, in whichsaid follow-on data is obtained at a time after acquisition of saidinitial data selected from the group consisting of:immediatelythereafter as in a continuous monitoring scenario; and after applicationof a suitable stress test; and after intervention; and after medicaltherapy;the benefit being identification of a subject who has undergonecardiac change.
 15. A noninvasive method of tracking cardiac status of asubject utilizing electrocardiography ECG data obtained therefrom as inclaim 12, which further comprises determining if said subject is at highrisk for sudden death by the additional steps of:a. determining thesubject's cardiac ejection fraction, (in percent); b. dividing said"score" by said cardiac ejection fraction, (in percent); and c.utilizing said output means, providing the result determined in step b.by use thereof, and if said result is greater observed than one (1.0),considering said subject as at high risk for sudden death.
 16. A methodof analyzing stored electrocardiography ECG data of a specific subject,comprising the steps of:a. obtaining data from ECG cycle(s) of saidsubject, and from subject(s) identified as normal, then selectingfrequency band(s), and separately applying necessary filteringtechniques to said data obtained from said subject and from saidsubject(s) identified as normal to separate the data obtained from saidsubject into at least one frequency band(s), and said data obtained fromsaid subject(s) identified as normal into essentally equivalentfrequency band(s); b. establishing criteria for, and in line therewithselecting some ECG cycle portion and arriving at representativeparameter(s) for each selected frequency band for data obtained fromeach of the subject and the subject(s) identified as normal; c.comparing said subject representative parameter(s) with correspondingsubject(s) identified as normal representative parameter(s); and d.combining selected differences between corresponding subject andsubject(s) identified as normal representative parameter(s) to arrive ata score, said score being the result of differences in magnitudes ofcorresponding subject and normal representative parameter(s); and e.providing an output means and presenting said score by use thereof. 17.A method of analyzing stored electrocardiography ECG data of a specificsubject as in claim 16, which further comprises determining if saidsubject is at high risk for sudden death by the additional steps of:a.determining the subject's cardiac ejection fraction, (in percent); andb. dividing said "score" by said cardiac ejection fraction, (inpercent); and c. utilizing said output means, providing the resultdetermined in step b. by use thereof, and if said result is greaterobserved than one (1.0), considering said subject as at high risk forsudden death.
 18. A noninvasive method of investigating cardiac statusof a subject utilizing electrocardiography ECG data obtained therefrom,said method enabling classification of said subject into normal andabnormal cardiac categories and determining if said subject is at highrisk for sudden death, said method comprising, in a functional sequence,performance of the steps of:a. obtaining data from ECG cycle(s) fromeach of a multiplicity of members of a population of subjects who havebeen documented as normal subjects, in that they do not show riskfactors for, or demonstrate detectable cardiac abnormality, byproviding, selecting and monitoring at least one lead(s) of said ECGsystem; b. establishing criteria for, and in line therewith selectingsome ECG cycle portion and defining cycle portion data pointstherewithin, and calculating an average selected ECG cycle portion dataset for said at least one monitored ECG system lead(s) by, for amonitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from each of a number of members ofsaid multiplicity of members of a population of subjects who have beendocumented as normal subjects, each said calculated average selected ECGcycle portion data set being a composite data set of said selected ECGcycle portion for said population of normal subjects, for a monitoredECG system lead; c. obtaining data from ECG cycle(s) from a subject, bymonitoring at least one lead(s) of said ECG system, said ECG systemlead(s) monitored being the same as the monitored ECG system lead(s)utilized in step a. to obtain data utilized in step b.; d. selectingsome ECG cycle portion, which is essentially that selected in step b.,and calculating an average selected ECG cycle portion data set for saidat least one monitored ECG system lead(s) by, for a monitored ECG systemlead, a procedure comprising combining corresponding ECG cycle portiondata points for said selected ECG cycle portion for ECG cycle(s)obtained from said subject, each said calculated average selected ECGcycle portion data set being a composite data set of said selected ECGcycle portion for said subject, for a monitored ECG system lead; e.calculating corresponding representative parameter(s) from resultingcomposite data sets calculated in steps b. and d., for monitored ECGsystem lead(s), for, respectively, said normal subject population andsaid subject; f. comparing subject to corresponding normal subjectpopulation representative parameter(s), and combining results thereof toarrive at a "score", the magnitude of which "score" results fromdifference(s) between magnitude(s) of corresponding normal subjectpopulation, and subject representative parameter(s), which "score"magnitude increases when said difference(s) in magnitude(s) betweencorresponding normal subject population, and subject, representativeparameter(s) increase, the magnitude of which "score" provides anindication of the cardiac status of said subject, with a "score" nearzero being indicative of a subject properly categorized as a cardiacnormal in that the magnitude(s) of subject representative parameter(s)are generally more closely matched to the magnitude(s) of correspondingnormal subject population representative parameter(s), and with aprogressively higher "score" being indicative of a subject progressivelymore properly categorized as a cardiac abnormal in that the magnitude(s)of subject representative parameter(s) are generally progressively lessclosely matched to the magnitude(s) of corresponding normal subjectpopulation representative parameter(s);said method further comprising asadditional steps at least one grouping of steps selected from the groupconsisting of: g., h. and i; j., k, and l; and m. and n.;said steps g.,h., and i., being: g. determining the subject's cardiac ejectionfraction, (in percent); h. dividing said "score" by said cardiacejection fraction, (in percent); i. providing an output means andpresenting the result provided in step h. therewith, and if said resultis determined to be greater than one (1.0), considering said subject asat high risk for sudden death;and said steps j., k., and l. being: j.providing at least a coordinate system consisting of magnitude vs. time,and optionally a coordinate system consisting of magnitude vs.frequency, and in step b. selecting portion of the ECG cycle includingthe region beyond the QRS complex and before the T wave; k. for said ECGcycle portion, performing calculations necessary to plot and displaynormal subject population and subject ECG data as a function of at leasttime and optionally frequency, to respectively provide as desired,visually interpretable plots of ECG magnitude and power spectral densitydata, observation of which provides an indication of the cardiac statusof said subject; and l. providing an output display means and visuallyplotting and displaying therewith at least a magnitude vs. time plot forsaid ECG cycle portion beyond the QRS complex and before the T wave,then noting if Rhomboids are therewithin, and if present, consideringsaid subject as at high risk for sudden death; andsaid steps m. and n.being: m. determining realtive magnitude pattern(s) amongst at least twosubject representative parameter values, and utilizing said pattern(s)as additional basis for insight to cardiac abnormality; and n. providingan output means, and via said output means obtaining and utilizing saidrelative magnitude pattern(s) as additional basis for investigating thecardiac status of said subject.
 19. A system for practicing anoninvasive method of investigating cardiac status of a subject andenabling classification of a subject into normal and abnormal cardiaccategories utilizing electrocardiography (ECG) data obtained therefromas in claim 18, which method further comprises performance of the stepof applying a confidence level acceptance test to results of comparingspecific subject, to corresponding specific normal subject population,representative parameter(s) prior to combining results thereof to arriveat a "score", said confidence level test comprising:a. the step ofdetermining mean and standard deviation acceptance parameters forspecific normal subject population representative parameter(s) basedupon the (ECG) data from which said composite data set of said selected(ECG) cycle portion for said population of normal subjects wascalculated; b. the step of determining an acceptance parameter for eachcorresponding specific subject representative parameter based upon the(ECG) data from which said composite data set of said selected (ECG)cycle portion for said subject was calculated; and c. the step ofaccepting the results of comparing a specific subject representativeparameter, to a corresponding specific normal subject populationrepresentative parameter, in arriving at said "score", only if theacceptance parameter for said specific subject representative parameteris set off by at least one associated normal subject populationacceptance standard deviation from the acceptance mean of saidcorresponding specific normal subject population representativeparameter.
 20. A noninvasive method of investigating cardiac status of asubject utilizing electrocardiography ECG data obtained therefrom, saidmethod enabling classification of said subject into normal and abnormalcardiac categories and determining if said subject is at high risk forsudden death, said method comprising, in a functional sequence,performance of the steps of:a. obtaining data from ECG cycle(s) fromeach of a multiplicity of members of a population of subjects who havebeen documented as normal subjects, in that they do not show riskfactors for, or demonstrate detectable cardiac abnormality, byproviding, selecting and monitoring at least one lead(s) of said ECGsystem; b. establishing criteria for, and in line therewith selectingsome ECG cycle portion, and defining cycle portion data pointstherewithin, and calculating an average selected ECG cycle portion dataset for at least one monitored ECG system lead(s) by, for a monitoredECG system lead, a procedure comprising combining corresponding ECGcycle portion data points for said selected ECG cycle portion for ECGcycle(s) obtained from each of a number of members of said multiplicityof members of a population of subjects who have been documented asnormal subjects, each said calculated average selected ECG cycle portiondata set being a composite data set of said selected ECG cycle portionfor said population of normal subjects, for a monitored ECG system lead;c. obtaining data from ECG cycle(s) from a subject, by monitoring atleast one lead(s) of ECG said system, said ECG system lead(s) monitoredbeing the same as the monitored ECG system lead(s) utilized in step a.to obtain data utilized in step b.; d. selecting some ECG cycle portion,which is essentially that selected in step b., and calculating anaverage selected ECG cycle portion data set for said at least onemonitored ECG system lead(s) by, for a monitored ECG system lead, aprocedure comprising combining corresponding ECG cycle portion datapoints for said selected ECG cycle portion for ECG cycle(s) obtainedfrom said subject, each said calculated average selected ECG cycleportion data set being a composite data set of said selected ECG cycleportion for said subject, for a monitored ECG system lead; e.calculating corresponding representative parameter(s) and correspondingratio(s) involving representative parameters from resulting compositedata sets calculated in steps b. and d., for monitored ECG systemlead(s), for, respectively, said normal subject population and saidsubject; f. comparing specific ratio(s) of subject to correspondingspecific ratio(s) of normal subject population representativeparameters, and combining results thereof to arrive at a "score", themagnitude of which "score" results from difference(s) betweenmagnitude(s) of specific corresponding ratio(s) of normal subjectpopulation, and ratio(s) of subject representative parameters, which"score" magnitude increases when said difference(s) in magnitude(s)between specific ratio(s) of corresponding normal subject population,and specific ratio(s) of subject representative parameters increase, themagnitude of which "score" provides an indication of the cardiac statusof said subject, with a "score" near zero being indicative of a subjectproperly categorized as a cardiac normal in that the magnitude(s) ofratio(s) of subject representative parameters are generally more closelymatched to the magnitude(s) of corresponding ratio(s) of normal subjectpopulation representative parameters, and with a progressively higher"score" being indicative of a subject progressively more properlycategorized as a cardiac abnormal in that the magnitude(s) of ratio(s)of subject representative parameters are generally progressively lessclosely matched to the magnitude(s) of ratio(s) of corresponding normalsubject population representative parameters;said method furthercomprising as additional steps at least one grouping of steps selectedfrom the group consisting of: g., h. and i; j., k, and l; and m. andn.;said steps g., h., and i., being: g. determining the subject'scardiac ejection fraction, (in percent); h. dividing said "score" bysaid cardiac ejection fraction, (in percent); i. providing an outputmeans and presenting the result provided in step h. therewith, and ifsaid result is determined to be greater than one (1.0), considering saidsubject as at high risk for sudden death;and said steps j., k., and l.being: j. providing at least a coordinate system consisting of magnitudevs. time, and optionally a coordinate system consisting of magnitude vs.frequency, and in step b. selecting a portion of the ECG cycle includingthe region beyond the QRS complex and before the T wave; k. for said ECGcycle portion, performing calculations necessary to plot and displaynormal subject population and subject ECG data as a function of at leasttime and optionally frequency, to respectively provide as desired,visually interpretable plots of ECG magnitude and power spectral densitydata, observation of which provides an indication of the cardiac statusof said subject; and l. providing an output display means and visuallyplotting and displaying therewith at least a magnitude vs. time plot forsaid ECG cycle portion beyond the QRS complex and before the T wave,then noting if Rhomboids are therewithin, and if present, consideringsaid subject as at high risk for sudden death; andsaid steps m. and n.being: m. determining realtive magnitude pattern(s) amongst at least tworatios of subject representative parameter values, and utilizing saidpattern(s) as additional basis for insight to cardiac abnormality; andn. providing an output means, and via said output means obtaining andutilizing said relative magnitude pattern(s) as additional basis forinvestigating the cardiac status of said subject.
 21. A system forpracticing a noninvasive method of investigating cardiac status of asubject and enabling classification of a subject into normal andabnormal cardiac categories utilizing electrocardiography (ECG) dataobtained therefrom as in claim 20, which method further comprisesperformance of the step of applying a confidence level acceptance testto results of comparing specific ratio(s) of subject, to correspondingspecific ratio(s) of normal subject population representative parametersprior to combining results thereof to arrive at a "score", saidconfidence level test comprising:a. the step of determining mean andstandard deviation acceptance parameters for specific ratio(s) of normalsubject population representative parameters based upon the (ECG) datafrom which said composite data set of said selected (ECG) cycle portionfor said population of normal subjects was calculated; b. the step ofdetermining an acceptance parameter for each corresponding specificratio of subject representative parameters based upon the (ECG) datafrom which said composite data set of said selected (ECG) cycle portionfor said subject was calculated; and c. the step of accepting theresults of comparing a specific ratio of subject representativeparameters, to a corresponding specific ratio of representativeparameters for said normal subject population, in arriving at said"score", only if said acceptance parameter for said specific ratio ofsaid subject representative parameters is set off by at least oneassociated normal subject population acceptance standard deviation fromthe acceptance mean of said corresponding specific ratio of normalsubject population representative parameters.
 22. A noninvasive methodof investigating cardiac status of a subject utilizingelectrocardiography ECG data obtained therefrom, said method enablingclassification of said subject into normal and abnormal cardiaccategories and determining if said subject is at high risk for suddendeath, said method comprising, in a functional sequence, performance ofthe steps of:a. obtaining data from ECG cycle(s) from each of amultiplicity of members of a population of subjects who have beendocumented as normal subjects, in that they do not show risk factorsfor, or demonstrate detectable cardiac abnormality, by providing,selecting and monitoring at least one lead(s) of an ECG system; b.establishing criteria for, and in line therewith selecting some ECGcycle portion and defining cycle portion data points therewithin, andcalculating an average selected ECG cycle portion data set for said atleast one monitored ECG system lead(s), by, for a monitored ECG systemlead, a procedure comprising combining corresponding ECG cycle portiondata points for said selected ECG cycle portion for ECG cycle(s)obtained from each of a number of said multiplicity of members of apopulation of subjects who have been documented as normal subjects, andselecting a plurality of frequency bands and applying filteringtechniques, to provide a plurality of data sets for said at least oneECG system lead(s) monitored, each said data set being a composite dataset of said selected ECG cycle portion for said population of normalsubjects in a monitored lead and selected frequency band range; c.obtaining data from ECG cycle(s) from a subject, by monitoring at leastone lead(s) of said ECG system, said ECG system lead(s) monitored beingthe same as the monitored ECG system lead(s) utilized in step a. toobtain data utilized in step b.; d. selecting some ECG cycle portion,said ECG cycle portion being essentially that selected in step b. forsaid normal subject population, and calculating an average selected ECGcycle portion data set for said at least one monitored ECG systemlead(s), by, for a monitored ECG system lead, a procedure comprisingcombining corresponding ECG cycle portion data points for said selectedECG cycle portion for subject ECG cycle(s), and selecting a plurality offrequency bands, said selected frequency bands being essentially thoseselected in step b. for said normal subject population, and applyingfiltering techniques which are essentially those applied in step b. forsaid normal subject population, to provide a plurality of data sets forsaid at least one monitored ECG system lead(s), each said data set beinga composite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. calculatingcorresponding representative parameter(s) and corresponding ratio(s)involving representative parameters from resulting composite data setscalculated in steps b. and d., in said selected frequency band rangesfor monitored ECG system lead(s), for respectively, said normal subjectpopulation and said subject; f. comparing specific subject andcorresponding specific normal subject population representativeparameter(s), and combining results thereof with the results ofcomparing specific ratio(s) of subject to corresponding specificratio(s) of normal subject population representative parameters, toarrive at a "score", the magnitude of which "score" results fromdifference(s) in magnitude(s) between corresponding subject and normalsubject population representative parameter(s) and difference(s) betweenmagnitude(s) of corresponding ratio(s) of normal subject population, andratio(s) of subject representative parameters, which "score" magnitudeincreases when difference(s) in magnitude(s) between correspondingsubject and normal subject population representative parameter(s)increase and difference(s) in magnitude(s) between ratio(s) ofcorresponding normal subject population, and ratio(s) of subjectrepresentative parameters increase, the magnitude of which "scores"provides an indication of the cardiac status of said subject, with a"score" near zero being indicative of a subject properly categorized asa cardiac normal in that magnitude(s) of subject representativeparameter(s) are generally more closely matched to the magnitude(s) ofcorresponding normal subject population representative parameter(s) andmagnitude(s) of ratio(s) of subject representative parameters aregenerally more closely matched to the magnitude(s) of correspondingratio(s) of normal subject population representative parameters, andwith a progressively higher "score" being indicative of a subjectprogressively more properly categorized as a cardiac abnormal in thatmagnitude(s) of subject representative parameter(s) are generallyprogressively less closely matched to the magnitude(s) of correspondingnormal subject population representative parameter(s) and magnitude(s)of ratio(s) of subject representative parameter(s) are generallyprogressively less closely matched to the magnitude(s) of ratio(s) ofcorresponding normal subject population representative parameters; saidmethod further comprising as additonal steps at least one grouping ofsteps selected from the group consisting of: g., h. and i; j., k, and l;and m. and n.;said steps g., h., and i., being: g. determining thesubject's cardiac ejection fraction, (in percent); h. dividing said"score" by said cardiac ejection fraction, (in percent); i. providing anoutput means and presenting the result provided in step h. therewith,and if said result is determined to be greater than one (1.0),considering said subject as at high risk for sudden death;and said stepsj., k., and l. being: j. providing at least a coordinate systemconsisting of magnitude vs. time, and optionally a coordinate systemconsisting of magnitude vs. frequency, and in step b. selecting aportion of the ECG cycle including the region beyond the QRS complex andbefore the T wave; k. for said ECG cycle portion, performingcalculations necessary to plot and display normal subject population andsubject ECG data as a function of at least time and optionallyfrequency, to respectively provide as desired, visually interpretableplots of ECG magnitude and power spectral density data, observation ofwhich provides an indication of the cardiac status of said subject; andl. providing an output display means and visually plotting anddisplaying therewith at least a magnitude vs. time plot for said ECGcycle portion beyond the QRS complex and before the T wave, then notingif Rhomboids are therewithin, and if present, considering said subjectas at high risk for sudden death; andsaid steps m. and n. being: m.determining realtive magnitude pattern(s) amongst at least one selectionfrom the group consisting of:subject represenatative parameter values;and ratios of subject representative parameter values; and n. providingan output means, and via said output means obtaining and utilizing saidrelative magnitude pattern(s) as additional basis for investigating thecardiac status of said subject.
 23. A system for practicing anoninvasive method of investigating cardiac status of a subject andenabling classification of a subject into normal and abnormal cardiaccategories utilizing electrocardiography (ECG) data obtained therefromas in claim 22, which method further comprises performance of the stepof applying a confidence level acceptance test to results of comparingsubject, to corresponding normal subject population representativeparameter(s), and the results of comparing ratios of subjectrepresentative parameters to corresponding ratios of normal subjectpopulation representative parameters, prior to combining results thereofto arrive at a "score", said confidence level test comprising:a. thestep of determining mean and standard deviation acceptance parametersfor specific normal subject population representative parameter(s) andspecific ratio(s) of normal subject population representative parametersbased upon the (ECG) data from which said composite data set of saidselected (ECG) cycle portion for said population of normal subjects wascalculated; b. the step of determining an acceptance parameter for eachcorresponding specific subject representative parameter and eachcorresponding specific ratio of subject representative parameters basedupon the (ECG) data from which said composite data set of said selected(ECG) cycle portion for said subject was calculated; and c. the step ofaccepting the results of comparing a specific subject representativeparameter to a corresponding specific normal subject populationrepresentative parameter in arriving at said "score", only if theacceptance parameter for said specific subject representative parameteris set off by at least one associated normal subject populationacceptance standard deviation from the acceptance mean of thecorresponding specific normal subject population representativeparameter; and accepting the results of comparing a specific ratio ofsubject representative parameters to a corresponding specific ratio ofrepresentative parameters for said normal subject population, inarriving at said "score", only if the acceptance parameter of saidspecific ratio of said subject representative parameters is set off byat least one associated normal subject population acceptance standarddeviation from the acceptance mean of said corresponding specific ratioof normal subject population representative parameters.
 24. A system forpracticing a noninvasive method of investigating cardiac status of asubject and enabling classification of a subject into normal andabnormal cardiac categories utilizing electrocardiography ECG dataobtained therefrom, said method comprising, in a functional sequence,performance of the steps of:a. obtaining data from ECG cycle(s) fromeach of a multiplicity of members of a population of subjects who havebeen documented as normal subjects, in that they do not show riskfactors for, or demonstrate detectable cardiac abnormality, byproviding, selecting and monitoring at least one lead(s) of an ECGsystem; b. establishing criteria for, and in line therewith selectingsome ECG cycle portion and calculating an average selected ECG cycleportion data set for at least one monitored ECG system lead(s), by, fora monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from each of a number of saidmultiplicity of members of a population of subjects who have beendocumented as normal subjects, and selecting a plurality of frequencybands and applying filtering techniques, to provide a plurality of datasets for said at least one ECG system lead(s) monitored, each said dataset being a composite data set of said selected ECG cycle portion forsaid population of normal subjects in a monitored lead and selectedfrequency band range; c. obtaining data from ECG cycle(s) from asubject, by monitoring at least one lead(s) of an ECG system, said ECGsystem lead(s) monitored being the same as the monitored ECG systemlead(s) utilized in step a. to obtain data utilized in step b.; d.selecting some ECG cycle portion, said ECG cycle portion beingessentially that selected in step b. for said normal subject population,and calculating an average selected ECG cycle portion data set for atleast one monitored ECG system lead(s), by, for a monitored ECG systemlead, a procedure comprising combining corresponding ECG cycle portiondata points for said selected ECG cycle portion for subject ECGcycle(s), and selecting a plurality of frequency bands, said selectedfrequency bands being essentially those selected in step b. for saidnormal subject population, and applying filtering techniques which areessentially those applied in step b. for said normal subject population,to provide a plurality of data sets for said at least one monitored ECGsystem lead(s), each said data set being a composite data set of saidselected ECG cycle portion for said subject in a monitored lead andselected frequency band range; e. providing an output display means andperforming at least one of the following steps f. and g.; f. calculatingmean and standard deviation representative parameters from at least oneresulting composite data set calculated in step b., said mean andstandard deviation parameters being from a selected frequency band rangefor a monitored ECG system lead, and providing a coordinate systemconsisting of magnitude vs. time on ordinate and abscissa respectivelyand plotting and displaying on said coordinate system loci consistingof:
 1. normal subject population standard deviation bounds located aboveand below said normal subject population mean, and2. a correspondingsubject data set, then observing differences between normal subjectpopulation and subject data plots, with a subject data set fallingwithin the normal subject standard deviation bounds being indicative ofa subject properly classified as a cardiac normal, and with a subjectdata set falling progressively further outside said normal subjectstandard deviation bounds being indicative of a subject progressivelymore properly classified as a cardiac abnormal; g. calculating powerspectral density data for at least one resulting composite data setcalculated in step b. and for a corresponding resulting composite dataset calculated in step d. for a selected frequency band range for amonitored ECG system lead, and providing a coordinate system consistingof magnitude vs. frequency on ordinate and abscissa respectively andplotting and displaying on said coordinate system power spectral densitydata loci, then observing differences between normal subject populationand subject data loci, with closely matched corresponding subject andnormal subject population data set power spectral density loci beingindicative of a subject properly classified as a cardiac normal and withprogressively more mismatched subject and normal subject population dataset power spectral density loci being indicative of a subjectprogressively more properly classified as a cardiac abnormal;said ECGsystem comprising ECG lead(s) which monitor electrodes affixed to asubject or member of a normal subject population, and which providemonitored signal(s) to an ECG monitor, said ECG monitor beingfunctionally interconnected to a computational means which is programmedto accept ECG data from said ECG monitor and practice the method ofsteps a.-e., said computational means being functionally interconnectedto an output means to enable practice of steps f. or g.
 25. Anoninvasive method of tracking cardiac status change in a subjectutilizing electrocardiography ECG data obtained therefrom, said methodcomprising, in a functional sequence, performance of the steps of:a.obtaining initial data from ECG cycles from a subject by providing,selecting and monitoring at least one lead(s) of said ECG system; b.establishing criteria for, and in line therewith selecting some ECGcycle portion and calculating an average selected ECG cycle portion dataset for at least one monitored ECG system lead(s), by, for a monitoredECG system lead, a procedure comprising combining corresponding ECGcycle portion data points for said selected ECG cycle portion for ECGcycle(s) obtained from said subject, and selecting a plurality offrequency bands and applying filtering techniques, to provide aplurality of data sets for said at least one ECG system lead(s)monitored, each said data set being an initial composite data set ofsaid selected ECG cycle portion for said subjects in a monitored leadand selected frequency band range; c. obtaining follow-on data from ECGcycle(s) from a subject at a later time, by monitoring at least onelead(s) of an ECG system, said ECG system lead(s) monitored being thesame as the monitored ECG system lead(s) utilized in step a. to obtaindata utilized in step b.; d. selecting some ECG cycle portion, said ECGcycle portion being essentially that selected in step b. for saidinitial subject data, and calculating an average selected ECG cycleportion data set for at least one monitored ECG system lead(s), by, fora monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for subject ECG cycle(s), and selecting a plurality of frequencybands, said selected frequency bands being essentially those selected instep b. for said initial subject data, and applying filtering techniqueswhich are essentially those applied in step b. for said initial subjectdata, to provide a plurality of data sets for said at least onemonitored ECG system lead(s), each said data set being a follow-oncomposite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. providing anoutput display means and performing at least one of the following stepsf. and g.; f. calculating mean and standard deviation representativeparameters from at least one resulting composite data set calculated instep b., said mean and standard deviation parameters being from aselected frequency band range for a monitored ECG system lead, andproviding a coordinate system consisting of magnitude vs. time onordinate and abscissa respectively and plotting and displaying on saidcoordinate system loci consisting of:1. initial subject standarddeviation bounds located above and below said initial subject data setmean, and
 2. a corresponding follow-on subject data set,then observingdifferences between initial subject and follow-on subject data plots,with a follow-on subject data set falling within the initial subjectdata set standard deviation bounds being indicative of a subjectproperly classified as having not undergone cardiac change, and with afollow-on subject data set falling progressively further outside saidinitial subject data set standard deviation bounds being indicative of asubject progressively more properly classified as having undergonecardiac change; g. calculating power spectral densilty data for at leastone resulting composite data set calculated in step b. and for acorresponding resulting composite data set calculated in step d. for aselected frequency band range for a monitored ECG system lead, andproviding a coordinate system consisting of magnitude vs. frequency onordinate and abscissa respectively and plotting and displaying on saidcoordinate system power spectral density data loci, then observingdifferences between initial subject and follow-on subject data loci,with closely matched corresponding initial subject and follow-on subjectdata set power spectral density loci being indicative of a subject whohas not undergone cardiac change and with progressively more mismatchedinitial subject and follow-on subject data set power spectral densityloci being indicative of a subject progressively more properlyclassified having undergone cardiac change.
 26. An ECG system forpracticing a noninvasive method of tracking cardiac status change in asubject utilizing electrocardiography ECG data obtained therefrom,enabling classification of said subject into normal and abnormal cardiaccategories utilizing electrocardiography ECG data obtained therefrom,said method comprising, in a functional sequence, performance of thesteps of:a. obtaining initial data from ECG cycles from a subject byproviding, selecting and monitoring at least one lead(s) of said ECGsystem; b. establishing criteria for, and in line therewith selectingsome ECG cycle portion and calculating an average selected ECG cycleportion data set for said at least one monitored ECG system lead(s) by,for a monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for ECG cycle(s) obtained from said subject, and selecting aplurality of frequency bands and applying filtering techniques, toprovide a plurality of data sets for said at least one ECG systemlead(s) monitored, each said data set being an initial composite dataset of said selected ECG cycle portion for said subjects in a monitoredlead and selected frequency band range; c. obtaining follow-on data fromECG cycle(s) from a subject at a later time, by monitoring said at leastone lead(s) of an ECG system, said ECG system lead(s) monitored beingthe same as the monitored ECG system lead(s) utilized in step a. toobtain data utilized in step b.; d. selecting some ECG cycle portion,said ECG cycle portion being essentially that selected in step b. forsaid initial subject data, and calculating an average selected ECG cycleportion data set for said at least one monitored ECG system lead(s), by,for a monitored ECG system lead, a procedure comprising combiningcorresponding ECG cycle portion data points for said selected ECG cycleportion for subject ECG cycle(s), and selecting a plurality of frequencybands, said selected frequency bands being essentially those selected instep b. for said initial subject data, and applying filtering techniqueswhich are essentially those applied in step b. for said initial subjectdata, to provide a plurality of data sets for said at least onemonitored ECG system lead(s), each said data set being a follow-oncomposite data set of said selected ECG cycle portion for said subjectin a monitored lead and selected frequency band range; e. providing anoutput display means and performing at least one of the following stepsf. and g.; f. calculating mean and standard deviation representativeparameters from at least one resulting composite data set calculated instep b., said mean and standard deviation parameters being from aselected frequency band range for a monitored ECG system lead, andproviding a coordinate system consisting of magnitude vs. time onordinate and abscissa respectively and plotting and displaying on saidcoordinate system loci consisting of:
 1. initial subject standarddeviation bounds located above and below said initial subject data setmean, and2. a corresponding follow-on subject data set,then observingdifferences between initial subject and follow-on subject data plots,with a follow-on subject data set falling within the initial subjectdata set standard deviation bounds being indicative of a subjectproperly classified as having not undergone cardiac change, and with afollow-on subject data set falling progressively further outside saidinitial subject data set standard deviation bounds being indicative of asubject progressively more properly classified as having undergonecardiac change; g. calculating power spectral density data for at leastone resulting composite data set calculated in step b. and for acorresponding resulting composite data set calculated in step d. for aselected frequency band range for a monitored ECG system lead, andproviding a coordinate system consisting of magnitude vs. frequency onordinate and abscissa respectively and plotting and displaying on saidcoordinate system power spectral density data loci, then observingdifferences between initial subject and follow-on subject data loci,with closely matched corresponding initial subject and follow-on subjectdata set power spectral density loci being indicative of a subject whohas not undergone cardiac change and with progressively more mismatchedinitial subject and follow-on subject data set power spectral densityloci being indicative of a subject progressively more properlyclassified having undergone cardiac change;said ECG system comprisingECG lead(s) which monitor electrodes affixed to a subject or member of anormal subject population, and which provide monitored signal(s) to anECG monitor, said ECG monitor being functionally interconnected to acomputational means which is programmed to accept ECG data from said ECGmonitor and practice the method of steps a.-e., said computational meansbeing functionally interconnected to an output means to enable practiceof steps f. or g.